>>Good morning fellow nerds and welcome back to AWS Reinvent. We are live from the show floor here in Las Vegas, Nevada. My name is Savannah Peterson, joined with my fabulous co-host John Furrier. Day two keynotes are rolling. >>Yeah. What do you thinking this? This is the day where everything comes, so the core gets popped off the bottle, all the announcements start flowing out tomorrow. You hear machine learning from swee lot more in depth around AI probably. And then developers with Verner Vos, the CTO who wrote the seminal paper in in early two thousands around web service that becames. So again, just another great year of next level cloud. Big discussion of data in the keynote bulk of the time was talking about data and business intelligence, business transformation easier. Is that what people want? They want the easy button and we're gonna talk a lot about that in this segment. I'm really looking forward to this interview. >>Easy button. We all want the >>Easy, we want the easy button. >>I love that you brought up champagne. It really feels like a champagne moment for the AWS community as a whole. Being here on the floor feels a bit like the before times. I don't want to jinx it. Our next guest, Scott Castle, from Si Sense. Thank you so much for joining us. How are you feeling? How's the show for you going so far? Oh, >>This is exciting. It's really great to see the changes that are coming in aws. It's great to see the, the excitement and the activity around how we can do so much more with data, with compute, with visualization, with reporting. It's fun. >>It is very fun. I just got a note. I think you have the coolest last name of anyone we've had on the show so far, castle. Oh, thank you. I'm here for it. I'm sure no one's ever said that before, but I'm so just in case our audience isn't familiar, tell us about >>Soy Sense is an embedded analytics platform. So we're used to take the queries and the analysis that you can power off of Aurora and Redshift and everything else and bring it to the end user in the applications they already know how to use. So it's all about embedding insights into tools. >>Embedded has been a, a real theme. Nobody wants to, it's I, I keep using the analogy of multiple tabs. Nobody wants to have to leave where they are. They want it all to come in there. Yep. Now this space is older than I think everyone at this table bis been around since 1958. Yep. How do you see Siente playing a role in the evolution there of we're in a different generation of analytics? >>Yeah, I mean, BI started, as you said, 58 with Peter Lu's paper that he wrote for IBM kind of get became popular in the late eighties and early nineties. And that was Gen one bi, that was Cognos and Business Objects and Lotus 1 23 think like green and black screen days. And the way things worked back then is if you ran a business and you wanted to get insights about that business, you went to it with a big check in your hand and said, Hey, can I have a report? And they'd come back and here's a report. And it wasn't quite right. You'd go back and cycle, cycle, cycle and eventually you'd get something. And it wasn't great. It wasn't all that accurate, but it's what we had. And then that whole thing changed in about two, 2004 when self-service BI became a thing. And the whole idea was instead of going to it with a big check in your hand, how about you make your own charts? >>And that was totally transformative. Everybody started doing this and it was great. And it was all built on semantic modeling and having very fast databases and data warehouses. Here's the problem, the tools to get to those insights needed to serve both business users like you and me and also power users who could do a lot more complex analysis and transformation. And as the tools got more complicated, the barrier to entry for everyday users got higher and higher and higher to the point where now you look, look at Gartner and Forester and IDC this year. They're all reporting in the same statistic. Between 10 and 20% of knowledge workers have learned business intelligence and everybody else is just waiting in line for a data analyst or a BI analyst to get a report for them. And that's why the focus on embedded is suddenly showing up so strong because little startups have been putting analytics into their products. People are seeing, oh my, this doesn't have to be hard. It can be easy, it can be intuitive, it can be native. Well why don't I have that for my whole business? So suddenly there's a lot of focus on how do we embed analytics seamlessly? How do we embed the investments people make in machine learning in data science? How do we bring those back to the users who can actually operationalize that? Yeah. And that's what Tysons does. Yeah. >>Yeah. It's interesting. Savannah, you know, data processing used to be what the IT department used to be called back in the day data processing. Now data processing is what everyone wants to do. There's a ton of data we got, we saw the keynote this morning at Adam Lesky. There was almost a standing of vision, big applause for his announcement around ML powered forecasting with Quick Site Cube. My point is people want automation. They want to have this embedded semantic layer in where they are not having all the process of ETL or all the muck that goes on with aligning the data. All this like a lot of stuff that goes on. How do you make it easier? >>Well, to be honest, I, I would argue that they don't want that. I think they, they think they want that, cuz that feels easier. But what users actually want is they want the insight, right? When they are about to make a decision. If you have a, you have an ML powered forecast, Andy Sense has had that built in for years, now you have an ML powered forecast. You don't need it two weeks before or a week after in a report somewhere. You need it when you're about to decide do I hire more salespeople or do I put a hundred grand into a marketing program? It's putting that insight at the point of decision that's important. And you don't wanna be waiting to dig through a lot of infrastructure to find it. You just want it when you need it. What's >>The alternative from a time standpoint? So real time insight, which is what you're saying. Yep. What's the alternative? If they don't have that, what's >>The alternative? Is what we are currently seeing in the market. You hire a bunch of BI analysts and data analysts to do the work for you and you hire enough that your business users can ask questions and get answers in a timely fashion. And by the way, if you're paying attention, there's not enough data analysts in the whole world to do that. Good luck. I am >>Time to get it. I really empathize with when I, I used to work for a 3D printing startup and I can, I have just, I mean, I would call it PTSD flashbacks of standing behind our BI guy with my list of queries and things that I wanted to learn more about our e-commerce platform in our, in our marketplace and community. And it would take weeks and I mean this was only in 2012. We're not talking 1958 here. We're talking, we're talking, well, a decade in, in startup years is, is a hundred years in the rest of the world life. But I think it's really interesting. So talk to us a little bit about infused and composable analytics. Sure. And how does this relate to embedded? Yeah. >>So embedded analytics for a long time was I want to take a dashboard I built in a BI environment. I wanna lift it and shift it into some other application so it's close to the user and that is the right direction to go. But going back to that statistic about how, hey, 10 to 20% of users know how to do something with that dashboard. Well how do you reach the rest of users? Yeah. When you think about breaking that up and making it more personalized so that instead of getting a dashboard embedded in a tool, you get individual insights, you get data visualizations, you get controls, maybe it's not even actually a visualization at all. Maybe it's just a query result that influences the ordering of a list. So like if you're a csm, you have a list of accounts in your book of business, you wanna rank those by who's priorities the most likely to churn. >>Yeah. You get that. How do you get that most likely to churn? You get it from your BI system. So how, but then the question is, how do I insert that back into the application that CSM is using? So that's what we talk about when we talk about Infusion. And SI started the infusion term about two years ago and now it's being used everywhere. We see it in marketing from Click and Tableau and from Looker just recently did a whole launch on infusion. The idea is you break this up into very small digestible pieces. You put those pieces into user experiences where they're relevant and when you need them. And to do that, you need a set of APIs, SDKs, to program it. But you also need a lot of very solid building blocks so that you're not building this from scratch, you're, you're assembling it from big pieces. >>And so what we do aty sense is we've got machine learning built in. We have an LQ built in. We have a whole bunch of AI powered features, including a knowledge graph that helps users find what else they need to know. And we, we provide those to our customers as building blocks so that they can put those into their own products, make them look and feel native and get that experience. In fact, one of the things that was most interesting this last couple of couple of quarters is that we built a technology demo. We integrated SI sensee with Office 365 with Google apps for business with Slack and MS teams. We literally just threw an Nlq box into Excel and now users can go in and say, Hey, which of my sales people in the northwest region are on track to meet their quota? And they just get the table back in Excel. They can build charts of it and PowerPoint. And then when they go to their q do their QBR next week or week after that, they just hit refresh to get live data. It makes it so much more digestible. And that's the whole point of infusion. It's bigger than just, yeah. The iframe based embedding or the JavaScript embedding we used to talk about four or five years >>Ago. APIs are very key. You brought that up. That's gonna be more of the integration piece. How does embedable and composable work as more people start getting on board? It's kind of like a Yeah. A flywheel. Yes. What, how do you guys see that progression? Cause everyone's copying you. We see that, but this is a, this means it's standard. People want this. Yeah. What's next? What's the, what's that next flywheel benefit that you guys coming out with >>Composability, fundamentally, if you read the Gartner analysis, right, they, when they talk about composable, they're talking about building pre-built analytics pieces in different business units for, for different purposes. And being able to plug those together. Think of like containers and services that can, that can talk to each other. You have a composition platform that can pull it into a presentation layer. Well, the presentation layer is where I focus. And so the, so for us, composable means I'm gonna have formulas and queries and widgets and charts and everything else that my, that my end users are gonna wanna say almost minority report style. If I'm not dating myself with that, I can put this card here, I can put that chart here. I can set these filters here and I get my own personalized view. But based on all the investments my organization's made in data and governance and quality so that all that infrastructure is supporting me without me worrying much about it. >>Well that's productivity on the user side. Talk about the software angle development. Yeah. Is your low code, no code? Is there coding involved? APIs are certainly the connective tissue. What's the impact to Yeah, the >>Developer. Oh. So if you were working on a traditional legacy BI platform, it's virtually impossible because this is an architectural thing that you have to be able to do. Every single tool that can make a chart has an API to embed that chart somewhere. But that's not the point. You need the life cycle automation to create models, to modify models, to create new dashboards and charts and queries on the fly. And be able to manage the whole life cycle of that. So that in your composable application, when you say, well I want chart and I want it to go here and I want it to do this and I want it to be filtered this way you can interact with the underlying platform. And most importantly, when you want to use big pieces like, Hey, I wanna forecast revenue for the next six months. You don't want it popping down into Python and writing that yourself. >>You wanna be able to say, okay, here's my forecasting algorithm. Here are the inputs, here's the dimensions, and then go and just put it somewhere for me. And so that's what you get withy sense. And there aren't any other analytics platforms that were built to do that. We were built that way because of our architecture. We're an API first product. But more importantly, most of the legacy BI tools are legacy. They're coming from that desktop single user, self-service, BI environment. And it's a small use case for them to go embedding. And so composable is kind of out of reach without a complete rebuild. Right? But with SI senses, because our bread and butter has always been embedding, it's all architected to be API first. It's integrated for software developers with gi, but it also has all those low code and no code capabilities for business users to do the minority report style thing. And it's assemble endless components into a workable digital workspace application. >>Talk about the strategy with aws. You're here at the ecosystem, you're in the ecosystem, you're leading product and they have a strategy. We know their strategy, they have some stuff, but then the ecosystem goes faster and ends up making a better product in most of the cases. If you compare, I know they'll take me to school on that, but I, that's pretty much what we report on. Mongo's doing a great job. They have databases. So you kind of see this balance. How are you guys playing in the ecosystem? What's the, what's the feedback? What's it like? What's going on? >>AWS is actually really our best partner. And the reason why is because AWS has been clear for many, many years. They build componentry, they build services, they build infrastructure, they build Redshift, they build all these different things, but they need, they need vendors to pull it all together into something usable. And fundamentally, that's what Cient does. I mean, we didn't invent sequel, right? We didn't invent jackal or dle. These are not, these are underlying analytics technologies, but we're taking the bricks out of the briefcase. We're assembling it into something that users can actually deploy for their use cases. And so for us, AWS is perfect because they focus on the hard bits. The the underlying technologies we assemble those make them usable for customers. And we get the distribution. And of course AWS loves that. Cause it drives more compute and it drives more, more consumption. >>How much do they pay you to say that >>Keynote, >>That was a wonderful pitch. That's >>Absolutely, we always say, hey, they got a lot of, they got a lot of great goodness in the cloud, but they're not always the best at the solutions and that they're trying to bring out, and you guys are making these solutions for customers. Yeah. That resonates with what they got with Amazon. For >>Example, we, last year we did a, a technology demo with Comprehend where we put comprehend inside of a semantic model and we would compile it and then send it back to Redshift. And it takes comprehend, which is a very cool service, but you kind of gotta be a coder to use it. >>I've been hear a lot of hype about the semantic layer. What is, what is going on with that >>Semantec layer is what connects the actual data, the tables in your database with how they're connected and what they mean so that a user like you or me who's saying I wanna bar chart with revenue over time can just work with revenue and time. And the semantic layer translates between what we did and what the database knows >>About. So it speaks English and then they converts it to data language. It's >>Exactly >>Right. >>Yeah. It's facilitating the exchange of information. And, and I love this. So I like that you actually talked about it in the beginning, the knowledge map and helping people figure out what they might not know. Yeah. I, I am not a bi analyst by trade and I, I don't always know what's possible to know. Yeah. And I think it's really great that you're doing that education piece. I'm sure, especially working with AWS companies, depending on their scale, that's gotta be a big part of it. How much is the community play a role in your product development? >>It's huge because I'll tell you, one of the challenges in embedding is someone who sees an amazing experience in outreach or in seismic. And to say, I want that. And I want it to be exactly the way my product is built, but I don't wanna learn a lot. And so you, what you want do is you want to have a community of people who have already built things who can help lead the way. And our community, we launched a new version of the SES community in early 2022 and we've seen a 450% growth in the c in that community. And we've gone from an average of one response, >>450%. I just wanna put a little exclamation point on that. Yeah, yeah. That's awesome. We, >>We've tripled our organic activity. So now if you post this Tysons community, it used to be, you'd get one response maybe from us, maybe from from a customer. Now it's up to three. And it's continuing to trend up. So we're, it's >>Amazing how much people are willing to help each other. If you just get in the platform, >>Do it. It's great. I mean, business is so >>Competitive. I think it's time for the, it's time. I think it's time. Instagram challenge. The reels on John. So we have a new thing. We're gonna run by you. Okay. We just call it the bumper sticker for reinvent. Instead of calling it the Instagram reels. If we're gonna do an Instagram reel for 30 seconds, what would be your take on what's going on this year at Reinvent? What you guys are doing? What's the most important story that you would share with folks on Instagram? >>You know, I think it's really what, what's been interesting to me is the, the story with Redshift composable, sorry. No, composable, Redshift Serverless. Yeah. One of the things I've been >>Seeing, we know you're thinking about composable a lot. Yes. Right? It's, it's just, it's in there, it's in your mouth. Yeah. >>So the fact that Redshift Serverless is now kind becoming the defacto standard, it changes something for, for my customers. Cuz one of the challenges with Redshift that I've seen in, in production is if as people use it more, you gotta get more boxes. You have to manage that. The fact that serverless is now available, it's, it's the default means it now people are just seeing Redshift as a very fast, very responsive repository. And that plays right into the story I'm telling cuz I'm telling them it's not that hard to put some analysis on top of things. So for me it's, it's a, maybe it's a narrow Instagram reel, but it's an >>Important one. Yeah. And that makes it better for you because you get to embed that. Yeah. And you get access to better data. Faster data. Yeah. Higher quality, relevant, updated. >>Yep. Awesome. As it goes into that 80% of knowledge workers, they have a consumer great expectation of experience. They're expecting that five ms response time. They're not waiting 2, 3, 4, 5, 10 seconds. They're not trained on theola expectations. And so it's, it matters a lot. >>Final question for you. Five years out from now, if things progress the way they're going with more innovation around data, this front end being very usable, semantic layer kicks in, you got the Lambda and you got serverless kind of coming in, helping out along the way. What's the experience gonna look like for a user? What's it in your mind's eye? What's that user look like? What's their experience? >>I, I think it shifts almost every role in a business towards being a quantitative one. Talking about, Hey, this is what I saw. This is my hypothesis and this is what came out of it. So here's what we should do next. I, I'm really excited to see that sort of scientific method move into more functions in the business. Cuz for decades it's been the domain of a few people like me doing strategy, but now I'm seeing it in CSMs, in support people and sales engineers and line engineers. That's gonna be a big shift. Awesome. >>Thank >>You Scott. Thank you so much. This has been a fantastic session. We wish you the best at si sense. John, always pleasure to share the, the stage with you. Thank you to everybody who's attuning in, tell us your thoughts. We're always eager to hear what, what features have got you most excited. And as you know, we will be live here from Las Vegas at reinvent from the show floor 10 to six all week except for Friday. We'll give you Friday off with John Furrier. My name's Savannah Peterson. We're the cube, the the, the leader in high tech coverage.

(upbeat music) (logo swooshing) >> Good morning and welcome back to Dallas, ladies and gentlemen, we are here with theCUBE Live from Supercomputing 2022. David, my cohost, how are you doing? Exciting, day two, feeling good? >> Very exciting. Ready to start off the day. >> Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >> Thank you for having us. >> Thank you for having us. >> I'm excited that you're starting off the day because we've been hearing a lot of rumors about Ethernet as the fabric for HPC, but we really haven't done a deep dive yet during the show. You all seem all in on Ethernet. Tell us about that. Armando, why don't you start? >> Yeah, I mean, when you look at Ethernet, customers are asking for flexibility and choice. So when you look at HPC, InfiniBand's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial in their enterprise customers. And not everybody wants to be in the top 500, what they want to do is improve their job time and improve their latency over the network. And when you look at Ethernet, you kind of look at the sweet spot between 8, 12, 16, 32 nodes, that's a perfect fit for Ethernet in that space and those types of jobs. >> I love that. Pete, you want to elaborate? >> Yeah, sure. I mean, I think one of the biggest things you find with Ethernet for HPC is that, if you look at where the different technologies have gone over time, you've had old technologies like, ATM, Sonic, Fifty, and pretty much everything is now kind of converged toward Ethernet. I mean, there's still some technologies such as InfiniBand, Omni-Path, that are out there. But basically, they're single source at this point. So what you see is that there is a huge ecosystem behind Ethernet. And you see that also the fact that Ethernet is used in the rest of the enterprise, is used in the cloud data centers, that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia into enterprise, into cloud service providers, it's much easier to integrate it with the same technology you're already using in those data centers, in those networks. >> So what's the state of the art for Ethernet right now? What's the leading edge? what's shipping now and what's in the near future? You're with Broadcom, you guys designed this stuff. >> Pete: Yeah. >> Savannah: Right. >> Yeah, so leading edge right now, got a couple things-- >> Savannah: We love good stage prop here on the theCUBE. >> Yeah, so this is Tomahawk 4. So this is what is in production, it's shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 terabytes per second. >> David: Okay. >> Which matches any other technology out there. Like if you look at say, InfinBand, highest they have right now that's just starting to get into production is 25.6 T. So state of the art right now is what we introduced, We announced this in August, This is Tomahawk 5, so this is 51.2 terabytes per second. So double the bandwidth, out of any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, actually, winds up being a factor of six efficiency. >> Savannah: Wow. >> 'Cause if you want, I can go into that, but... >> Why not? >> Well, what I want to know, please tell me that in your labs, you have a poster on the wall that says T five, with some like Terminator kind of character. (all laughs) 'Cause that would be cool. If it's not true, just don't say anything. I'll just... >> Pete: This can actually shift into a terminator. >> Well, so this is from a switching perspective. >> Yeah. >> When we talk about the end nodes, when we talk about creating a fabric, what's the latest in terms of, well, the nicks that are going in there, what speed are we talking about today? >> So as far as 30 speeds, it tends to be 50 gigabits per second. >> David: Okay. >> Moving to a hundred gig PAM-4. >> David: Okay. >> And we do see a lot of nicks in the 200 gig Ethernet port speed. So that would be four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon, 800 gig in the future. But say state of the art right now, we're seeing for the end node tends to be 200 gig E based on 50 gig PAM-4. >> Wow. >> Yeah, that's crazy. >> Yeah, that is great. My mind is act actively blown. I want to circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen, where do you think we are on the adoption curve and sort of in that cycle? Armando, do you want to go? >> Yeah, well, if you look at the market research, they're actually telling you it's 50/50 now. So Ethernet is at the level of 50%, InfinBand's at 50%, right? >> Savannah: Interesting. >> Yeah, and so what's interesting to us, customers are coming to us and say, hey, we want to see flexibility and choice and, hey, let's look at Ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we their have switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially MPI. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, hey, I've been InfiniBand but now I want to go Ethernet, there's going to be some learning curves there. And so what we want to do is really simplify that so that we can make it easy to install, get the cluster up and running and they can actually get some value out the cluster. >> Yeah, Pete, talk about that partnership. what does that look like? I mean, are you working with Dell before the T six comes out? Or you just say what would be cool is we'll put this in the T six? >> No, we've had a very long partnership both on the hardware and the software side. Dell's been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group, within Broadcom, we've then gotten very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, Dell can take it and deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again, in both the hardware and the software. >> So I'm fascinated by... I always like to know like what, yeah, exactly. Look, you start talking about the largest supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be two million CPUs, 2 million CPU cores. Exoflap of performance. What are the outward limits of T five in switches, building out a fabric, what does that look like? What are the increments in terms of how many... And I know it's a depends answer, but how many nodes can you support in a scale out cluster before you need another switch? Or what does that increment of scale look like today? >> Yeah, so this is 51.2 terabytes per second. Where we see the most common implementation based on this would be with 400 gig Ethernet ports. >> David: Okay. >> So that would be 128, 400 gig E ports connected to one chip. Now, if you went to 200 gig, which is kind of the state of the art for the nicks, you can have double that. So in a single hop, you can have 256 end nodes connected through one switch. >> Okay, so this T five, that thing right there, (all laughing) inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what's the form factor look like for where that T five sits? Is there just one in a chassis or you have.. What does that look like? >> It tends to be pizza boxes these days. What you've seen overall is that the industry's moved away from chassis for these high end systems more towardS pizza boxes. And you can have composable systems where, in the past you would have line cards, either the fabric cards that the line cards are plug into or interfaced to. These days what tends to happen is you'd have a pizza box and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the line card. >> David: Okay. >> So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a 2RU, with 64 OSFP ports. And often each of those OSFP, which is an 800 gig E or 800 gig port, we've broken out into two 400 gig ports. >> So yeah, in 2RU, and this is all air cooled, in 2RU, you've got 51.2 T. We do see some cases where customers would like to have different optics and they'll actually deploy 4RU, just so that way they have the phase-space density. So they can plug in 128, say QSFP 112. But yeah, it really depends on which optics, if you want to have DAK connectivity combined with optics. But those are the two most common form factors. >> And Armando, Ethernet isn't necessarily Ethernet in the sense that many protocols can be run over it. >> Right. >> I think I have a projector at home that's actually using Ethernet physical connections. But, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center Ethernet, or is this RDMA over converged Ethernet? What Are we talking about? >> Yeah, so RDMA, right? So when you look at running, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on Ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on Ethernet. If you look at NPIs officially, built to, hey, it was designed to run on InfiniBand but now what you see with Broadcom, with the great work they're doing, now we can make that work on Ethernet and get same performance, so that's huge for customers. >> Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of Ethernet in HPC in terms of AI and ML, where do you think we're going to be next year or 10 years from now? >> You want to go first or you want me to go first? >> I can start, yeah. >> Savannah: Pete feels ready. >> So I mean, what I see, I mean, Ethernet, what we've seen is that as far as on, starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. >> That's impressive. >> Pete: Yeah. >> Nicely done, casual, humble brag there. That was great, I love that. I'm here for you. >> I mean, I think that's one of the benefits of Ethernet, is the ecosystem, is the trajectory the roadmap we've had, I mean, you don't see that in any of the networking technology. >> David: More who? (all laughing) >> So I see that, that trajectory is going to continue as far as the switches doubling in bandwidth, I think that they're evolving protocols, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on RDMA, for the supercomputing, the AI/ML workloads. But we do see that as you have a mix of the applications running on these end nodes, maybe they're interfacing to the CPUs for some processing, you might use a different mix of protocols. So I'd say it's going to be doubling a bandwidth over time, evolution of the protocols. I mean, I expect that Rocky is probably going to evolve over time depending on the AI/ML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-packed optics. So right now, this chip is, all the balls in the back here, there's electrical connections. >> How many are there, by the way? 9,000 plus on the back of that-- >> 9,352. >> I love how specific it is. It's brilliant. >> Yeah, so right now, all the SERDES, all the signals are coming out electrically based, but we've actually shown, we actually we have a version of Tomahawk 4 at 25.6 T that has co-packed optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk 5. >> Nice. >> Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. >> Wow. Cool. >> So I see there's the bandwidth, there's radix's increasing, protocols, different physical connectivity. So I think there's a lot of things throughout, and the protocol stack's also evolving. So a lot of excitement, a lot of new technology coming to bear. >> Okay, You just threw a carrot down the rabbit hole. I'm only going to chase this one, okay? >> Peter: All right. >> So I think of individual discreet physical connections to the back of those balls. >> Yeah. >> So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that many optical connections? What's the mapping there? What does that look like? >> So what we've announced for Tomahawk 5 is it would have FR4 optics coming out. So you'd actually have 512 fiber pairs coming out. So basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because-- >> It's miraculous, essentially. >> Savannah: I know. >> Yeah. So a lot of people are going to be looking at this and thinking in terms of InfiniBand versus Ethernet, I think you've highlighted some of the benefits of specifically running Ethernet moving forward as HPC which sort of just trails slightly behind super computing as we define it, becomes more pervasive AI/ML. What are some of the other things that maybe people might not immediately think about when they think about the advantages of running Ethernet in that environment? Is it about connecting the HPC part of their business into the rest of it? What are the advantages? >> Yeah, I mean, that's a big thing. I think, and one of the biggest things that Ethernet has again, is that the data centers, the networks within enterprises, within clouds right now are run on Ethernet. So now, if you want to add services for your customers, the easiest thing for you to do is the drop in clusters that are connected with the same networking technology. So I think one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to Ethernet. So now you've got to train your technicians, you train your assist admins on two different network technologies. You need to have all the debug technology, all the interconnect for that. So here, the easiest thing is you can use Ethernet, it's going to give you the same performance and actually, in some cases, we've seen better performance than we've seen with Omni-Path, better than in InfiniBand. >> That's awesome. Armando, we didn't get to you, so I want to make sure we get your future hot take. Where do you see the future of Ethernet here in HPC? >> Well, Pete hit on a big thing is bandwidth, right? So when you look at, train a model, okay? So when you go and train a model in AI, you need to have a lot of data in order to train that model, right? So what you do is essentially, you build a model, you choose whatever neural network you want to utilize. But if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the CPU. And essentially, if you're going to do it maybe on CPU only, but if you do it on accelerators, well, guess what? You need a big pipe in order to get all that data through. And here's the deal, the bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage, maybe it's some new way you design a product, but that's a benefit of speed, you want faster, faster, faster. >> It's all about making it faster and easier-- for the users. >> Armando: It is. >> I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas, stakes, there's a lot going on with that. >> Making me hungry. >> I know, exactly. I'm sitting out here thinking, man, I did not have big enough breakfast. How did you come up with the name Tomahawk? >> So Tomahawk, I think it just came from a list. So we have a tried end product line. >> Savannah: Ah, yes. >> Which is a missile product line. And Tomahawk is being kind of like the bigger and batter missile, so. >> Savannah: Love this. Yeah, I mean-- >> So do you like your engineers? You get to name it. >> Had to ask. >> It's collaborative. >> Okay. >> We want to make sure everyone's in sync with it. >> So just it's not the Aquaman tried. >> Right. >> It's the steak Tomahawk. I think we're good now. >> Now that we've cleared that-- >> Now we've cleared that up. >> Armando, Pete, it was really nice to have both you. Thank you for teaching us about the future of Ethernet and HCP. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to theCUBE live from Dallas. We're here talking all things HPC and supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us. (soft music)

>>You can put this in a conference. >>Good morning and welcome back to Dallas. Ladies and gentlemen, we are here with the cube Live from, from Supercomputing 2022. David, my cohost, how you doing? Exciting. Day two. Feeling good. >>Very exciting. Ready to start off the >>Day. Very excited. We have two fascinating guests joining us to kick us off. Please welcome Pete and Armando. Gentlemen, thank you for being here with us. >>Having us, >>For having us. I'm excited that you're starting off the day because we've been hearing a lot of rumors about ethernet as the fabric for hpc, but we really haven't done a deep dive yet during the show. Y'all seem all in on ethernet. Tell us about that. Armando, why don't you start? >>Yeah. I mean, when you look at ethernet, customers are asking for flexibility and choice. So when you look at HPC and you know, infinite band's always been around, right? But when you look at where Ethernet's coming in, it's really our commercial and their enterprise customers. And not everybody wants to be in the top 500. What they want to do is improve their job time and improve their latency over the network. And when you look at ethernet, you kinda look at the sweet spot between 8, 12, 16, 32 nodes. That's a perfect fit for ethernet and that space and, and those types of jobs. >>I love that. Pete, you wanna elaborate? Yeah, yeah, >>Yeah, sure. I mean, I think, you know, one of the biggest things you find with internet for HPC is that, you know, if you look at where the different technologies have gone over time, you know, you've had old technologies like, you know, atm, Sonic, fitty, you know, and pretty much everything is now kind of converged toward ethernet. I mean, there's still some technologies such as, you know, InfiniBand, omnipath that are out there. Yeah. But basically there's single source at this point. So, you know, what you see is that there is a huge ecosystem behind ethernet. And you see that also, the fact that ethernet is used in the rest of the enterprise is using the cloud data centers that is very easy to integrate HPC based systems into those systems. So as you move HPC out of academia, you know, into, you know, into enterprise, into cloud service providers is much easier to integrate it with the same technology you're already using in those data centers, in those networks. >>So, so what's this, what is, what's the state of the art for ethernet right now? What, you know, what's, what's the leading edge, what's shipping now and what and what's in the near future? You, you were with Broadcom, you guys design this stuff. >>Yeah, yeah. Right. Yeah. So leading edge right now, I got a couple, you know, Wes stage >>Trough here on the cube. Yeah. >>So this is Tomahawk four. So this is what is in production is shipping in large data centers worldwide. We started sampling this in 2019, started going into data centers in 2020. And this is 25.6 tets per second. Okay. Which matches any other technology out there. Like if you look at say, infin band, highest they have right now that's just starting to get into production is 25 point sixt. So state of the art right now is what we introduced. We announced this in August. This is Tomahawk five. So this is 51.2 terabytes per second. So double the bandwidth have, you know, any other technology that's out there. And the important thing about networking technology is when you double the bandwidth, you don't just double the efficiency, it's actually winds up being a factor of six efficiency. Wow. Cause if you want, I can go into that, but why >>Not? Well, I, what I wanna know, please tell me that in your labs you have a poster on the wall that says T five with, with some like Terminator kind of character. Cause that would be cool if it's not true. Don't just don't say anything. I just want, I can actually shift visual >>It into a terminator. So. >>Well, but so what, what are the, what are the, so this is, this is from a switching perspective. Yeah. When we talk about the end nodes, when we talk about creating a fabric, what, what's, what's the latest in terms of, well, the kns that are, that are going in there, what's, what speed are we talking about today? >>So as far as 30 speeds, it tends to be 50 gigabits per second. Okay. Moving to a hundred gig pan four. Okay. And we do see a lot of Knicks in the 200 gig ethernet port speed. So that would be, you know, four lanes, 50 gig. But we do see that advancing to 400 gig fairly soon. 800 gig in the future. But say state of the art right now, we're seeing for the end nodes tends to be 200 gig E based on 50 gig pan four. Wow. >>Yeah. That's crazy. Yeah, >>That is, that is great. My mind is act actively blown. I wanna circle back to something that you brought up a second ago, which I think is really astute. When you talked about HPC moving from academia into enterprise, you're both seeing this happen. Where do you think we are on the adoption curve and sort of in that cycle? Armand, do you wanna go? >>Yeah, yeah. Well, if you look at the market research, they're actually telling it's 50 50 now. So ethernet is at the level of 50%. InfiniBand is at 50%. Right. Interesting. Yeah. And so what's interesting to us, customers are coming to us and say, Hey, we want to see, you know, flexibility and choice and hey, let's look at ethernet and let's look at InfiniBand. But what is interesting about this is that we're working with Broadcom, we have their chips in our lab, we have our switches in our lab. And really what we're trying to do is make it easy to simple and configure the network for essentially mpi. And so the goal here with our validated designs is really to simplify this. So if you have a customer that, Hey, I've been in fbe, but now I want to go ethernet, you know, there's gonna be some learning curves there. And so what we wanna do is really simplify that so that we can make it easy to install, get the cluster up and running, and they can actually get some value out of the cluster. >>Yeah. Peter, what, talk about that partnership. What, what, what does that look like? Is it, is it, I mean, are you, you working with Dell before the, you know, before the T six comes out? Or you just say, you know, what would be cool, what would be cool is we'll put this in the T six? >>No, we've had a very long partnership both on the hardware and the software side. You know, Dell has been an early adopter of our silicon. We've worked very closely on SI and Sonic on the operating system, you know, and they provide very valuable feedback for us on our roadmap. So before we put out a new chip, and we have actually three different product lines within the switching group within Broadcom, we've then gotten, you know, very valuable feedback on the hardware and on the APIs, on the operating system that goes on top of those chips. So that way when it comes to market, you know, Dell can take it and, you know, deliver the exact features that they have in the current generation to their customers to have that continuity. And also they give us feedback on the next gen features they'd like to see again in both the hardware and the software. >>So, so I, I'm, I'm just, I'm fascinated by, I I, I always like to know kind like what Yeah, exactly. Exactly right. Look, you, you start talking about the largest super supercomputers, most powerful supercomputers that exist today, and you start looking at the specs and there might be 2 million CPUs, 2 million CPU cores, yeah. Ex alop of, of, of, of performance. What are the, what are the outward limits of T five in switches, building out a fabric, what does that look like? What are the, what are the increments in terms of how many, and I know it, I know it's a depends answer, but, but, but how many nodes can you support in a, in a, in a scale out cluster before you need another switch? What does that increment of scale look like today? >>Yeah, so I think, so this is 51.2 terras per second. What we see the most common implementation based on this would be with 400 gig ethernet ports. Okay. So that would be 128, you know, 400 giggi ports connected to, to one chip. Okay. Now, if you went to 200 gig, which is kind of the state of the art for the Nicks, you can have double that. Okay. So, you know, in a single hop you can have 256 end nodes connected through one switch. >>So, okay, so this T five, that thing right there inside a sheet metal box, obviously you've got a bunch of ports coming out of that. So what is, what does that, what's the form factor look like for that, for where that T five sits? Is there just one in a chassis or you have, what does that look >>Like? It tends to be pizza boxes these days. Okay. What you've seen overall is that the industry's moved away from chassis for these high end systems more towards pizza, pizza boxes. And you can have composable systems where, you know, in the past you would have line cards, either the fabric cards that the line cards are plugged into or interface to these days, what tends to happen is you'd have a pizza box, and if you wanted to build up like a virtual chassis, what you would do is use one of those pizza boxes as the fabric card, one of them as the, the line card. >>Okay. >>So what we see, the most common form factor for this is they tend to be two, I'd say for North America, most common would be a two R U with 64 OSF P ports. And often each of those OSF p, which is an 800 gig e or 800 gig port, we've broken out into two 400 gig quarts. Okay. So yeah, in two r u you've got, and this is all air cooled, you know, in two re you've got 51.2 T. We do see some cases where customers would like to have different optics, and they'll actually deploy a four U just so that way they have the face place density, so they can plug in 128, say qsf P one 12. But yeah, it really depends on which optics, if you wanna have DAK connectivity combined with, with optics. But those are the two most common form factors. >>And, and Armando ethernet isn't, ethernet isn't necessarily ethernet in the sense that many protocols can be run over it. Right. I think I have a projector at home that's actually using ethernet physical connections. But what, so what are we talking about here in terms of the actual protocol that's running over this? Is this exactly the same as what you think of as data center ethernet, or, or is this, you know, RDMA over converged ethernet? What, what are >>We talking about? Yeah, so our rdma, right? So when you look at, you know, running, you know, essentially HPC workloads, you have the NPI protocol, so message passing interface, right? And so what you need to do is you may need to make sure that that NPI message passing interface runs efficiently on ethernet. And so this is why we want to test and validate all these different things to make sure that that protocol runs really, really fast on ethernet, if you look at NPI is officially, you know, built to, Hey, it was designed to run on InfiniBand, but now what you see with Broadcom and the great work they're doing now, we can make that work on ethernet and get, you know, it's same performance. So that's huge for customers. >>Both of you get to see a lot of different types of customers. I kind of feel like you're a little bit of a, a looking into the crystal ball type because you essentially get to see the future knowing what people are trying to achieve moving forward. Talk to us about the future of ethernet in hpc in terms of AI and ml. Where, where do you think we're gonna be next year or 10 years from now? >>You wanna go first or you want me to go first? I can start. >>Yeah. Pete feels ready. >>So I mean, what I see, I mean, ethernet, I mean, is what we've seen is that as far as on the starting off of the switch side, is that we've consistently doubled the bandwidth every 18 to 24 months. That's >>Impressive. >>Yeah. So nicely >>Done, casual, humble brag there. That was great. That was great. I love that. >>I'm here for you. I mean, I think that's one of the benefits of, of Ethan is like, is the ecosystem, is the trajectory, the roadmap we've had, I mean, you don't see that in any other networking technology >>More who, >>So, you know, I see that, you know, that trajectory is gonna continue as far as the switches, you know, doubling in bandwidth. I think that, you know, they're evolving protocols. You know, especially again, as you're moving away from academia into the enterprise, into cloud data centers, you need to have a combination of protocols. So you'll probably focus still on rdma, you know, for the supercomputing, the a AIML workloads. But we do see that, you know, as you have, you know, a mix of the applications running on these end nodes, maybe they're interfacing to the, the CPUs for some processing, you might use a different mix of protocols. So I'd say it's gonna be doubling a bandwidth over time evolution of the protocols. I mean, I expect that Rocky is probably gonna evolve over time depending on the a AIML and the HPC workloads. I think also there's a big change coming as far as the physical connectivity within the data center. Like one thing we've been focusing on is co-pack optics. So, you know, right now this chip is all, all the balls in the back here, there's electrical connections. How >>Many are there, by the way? 9,000 plus on the back of that >>352. >>I love how specific it is. It's brilliant. >>Yeah. So we get, so right now, you know, all the thirties, all the signals are coming out electrically based, but we've actually shown, we have this, actually, we have a version of Hawk four at 25 point sixt that has co-pack optics. So instead of having electrical output, you actually have optics directly out of the package. And if you look at, we'll have a version of Tomahawk five Nice. Where it's actually even a smaller form factor than this, where instead of having the electrical output from the bottom, you actually have fibers that plug directly into the sides. Wow. Cool. So I see, you know, there's, you know, the bandwidth, there's radis increasing protocols, different physical connectivity. So I think there's, you know, a lot of things throughout, and the protocol stack's also evolving. So, you know, a lot of excitement, a lot of new technology coming to bear. >>Okay. You just threw a carrot down the rabbit hole. I'm only gonna chase this one. Okay. >>All right. >>So I think of, I think of individual discreet physical connections to the back of those balls. Yeah. So if there's 9,000, fill in the blank, that's how many connections there are. How do you do that in many optical connections? What's, what's, what's the mapping there? What does that, what does that look like? >>So what we've announced for TAMA five is it would have fr four optics coming out. So you'd actually have, you know, 512 fiber pairs coming out. So you'd have, you know, basically on all four sides, you'd have these fiber ribbons that come in and connect. There's actually fibers coming out of the, the sides there. We wind up having, actually, I think in this case, we would actually have 512 channels and it would wind up being on 128 actual fiber pairs because >>It's, it's miraculous, essentially. It's, I know. Yeah, yeah, yeah, yeah. Yeah. So, so, you know, a lot of people are gonna be looking at this and thinking in terms of InfiniBand versus versus ethernet. I think you've highlighted some of the benefits of specifically running ethernet moving forward as, as hpc, you know, which is sort of just trails slightly behind supercomputing as we define it, becomes more pervasive AI ml. What, what are some of the other things that maybe people might not immediately think about when they think about the advantages of running ethernet in that environment? Is it, is it connecting, is it about connecting the HPC part of their business into the rest of it? What, or what, what are the advantages? >>Yeah, I mean, that's a big thing. I think, and one of the biggest things that ethernet has again, is that, you know, the data centers, you know, the networks within enterprises within, you know, clouds right now are run on ethernet. So now if you want to add services for your customers, the easiest thing for you to do is, you know, the drop in clusters that are connected with the same networking technology, you know, so I think what, you know, one of the biggest things there is that if you look at what's happening with some of the other proprietary technologies, I mean, in some cases they'll have two different types of networking technologies before they interface to ethernet. So now you've got to train your technicians, you train your, your assist admins on two different network technologies. You need to have all the, the debug technology, all the interconnect for that. So here, the easiest thing is you can use ethernet, it's gonna give you the same performance. And actually in some cases we seen better performance than we've seen with omnipath than, you know, better than in InfiniBand. >>That's awesome. Armando, we didn't get to you, so I wanna make sure we get your future hot take. Where do you see the future of ethernet here in hpc? >>Well, Pete hit on a big thing is bandwidth, right? So when you look at train a model, okay, so when you go and train a model in ai, you need to have a lot of data in order to train that model, right? So what you do is essentially you build a model, you choose whatever neural network you wanna utilize, but if you don't have a good data set that's trained over that model, you can't essentially train the model. So if you have bandwidth, you want big pipes because you have to move that data set from the storage to the cpu. And essentially, if you're gonna do it maybe on CPU only, but if you do it on accelerators, well guess what? You need a big pipe in order to get all that data through. And here's the deal. The bigger the pipe you have, the more data, the faster you can train that model. So the faster you can train that model, guess what? The faster you get to some new insight, maybe it's a new competitive advantage. Maybe it's some new way you design a product, but that's a benefit of speed you want faster, faster, faster. >>It's all about making it faster and easier. It is for, for the users. I love that. Last question for you, Pete, just because you've said Tomahawk seven times, and I'm thinking we're in Texas Stakes, there's a lot going on with with that making >>Me hungry. >>I know exactly. I'm sitting up here thinking, man, I did not have a big enough breakfast. How do you come up with the name Tomahawk? >>So Tomahawk, I think you just came, came from a list. So we had, we have a tri end product line. Ah, a missile product line. And Tomahawk is being kinda like, you know, the bigger and batter missile, so, oh, okay. >>Love this. Yeah, I, well, I >>Mean, so you let your engineers, you get to name it >>Had to ask. It's >>Collaborative. Oh good. I wanna make sure everyone's in sync with it. >>So just so we, it's not the Aquaman tried. Right, >>Right. >>The steak Tomahawk. I >>Think we're, we're good now. Now that we've cleared that up. Now we've cleared >>That up. >>Armando P, it was really nice to have both you. Thank you for teaching us about the future of ethernet N hpc. David Nicholson, always a pleasure to share the stage with you. And thank you all for tuning in to the Cube Live from Dallas. We're here talking all things HPC and Supercomputing all day long. We hope you'll continue to tune in. My name's Savannah Peterson, thanks for joining us.

>>Collibra is a company that was founded in 2008 right before the so-called modern big data era kicked into high gear. The company was one of the first to focus its business on data governance. Now, historically, data governance and data quality initiatives, they were back office functions and they were largely confined to regulatory regulated industries that had to comply with public policy mandates. But as the cloud went mainstream, the tech giants showed us how valuable data could become and the value proposition for data quality and trust. It evolved from primarily a compliance driven issue to becoming a lynchpin of competitive advantage. But data in the decade of the 2010s was largely about getting the technology to work. You had these highly centralized technical teams that were formed and they had hyper specialized skills to develop data architectures and processes to serve the myriad data needs of organizations. >>And it resulted in a lot of frustration with data initiatives for most organizations that didn't have the resources of the cloud guys and the social media giants to really attack their data problems and turn data into gold. This is why today for example, this quite a bit of momentum to rethinking monolithic data architectures. You see, you hear about initiatives like data mesh and the idea of data as a product. They're gaining traction as a way to better serve the the data needs of decentralized business Uni users, you hear a lot about data democratization. So these decentralization efforts around data, they're great, but they create a new set of problems. Specifically, how do you deliver like a self-service infrastructure to business users and domain experts? Now the cloud is definitely helping with that, but also how do you automate governance? This becomes especially tricky as protecting data privacy has become more and more important. >>In other words, while it's enticing to experiment and run fast and loose with data initiatives kinda like the Wild West, to find new veins of gold, it has to be done responsibly. As such, the idea of data governance has had to evolve to become more automated. And intelligence governance and data lineage is still fundamental to ensuring trust as data. It moves like water through an organization. No one is gonna use data that isn't trusted. Metadata has become increasingly important for data discovery and data classification. As data flows through an organization, the continuously ability to check for data flaws and automating that data quality, they become a functional requirement of any modern data management platform. And finally, data privacy has become a critical adjacency to cyber security. So you can see how data governance has evolved into a much richer set of capabilities than it was 10 or 15 years ago. >>Hello and welcome to the Cube's coverage of Data Citizens made possible by Calibra, a leader in so-called Data intelligence and the host of Data Citizens 2022, which is taking place in San Diego. My name is Dave Ante and I'm one of the hosts of our program, which is running in parallel to data citizens. Now at the Cube we like to say we extract the signal from the noise, and over the, the next couple of days, we're gonna feature some of the themes from the keynote speakers at Data Citizens and we'll hear from several of the executives. Felix Von Dala, who is the co-founder and CEO of Collibra, will join us along with one of the other founders of Collibra, Stan Christians, who's gonna join my colleague Lisa Martin. I'm gonna also sit down with Laura Sellers, she's the Chief Product Officer at Collibra. We'll talk about some of the, the announcements and innovations they're making at the event, and then we'll dig in further to data quality with Kirk Hasselbeck. >>He's the vice president of Data quality at Collibra. He's an amazingly smart dude who founded Owl dq, a company that he sold to Col to Collibra last year. Now many companies, they didn't make it through the Hado era, you know, they missed the industry waves and they became Driftwood. Collibra, on the other hand, has evolved its business. They've leveraged the cloud, expanded its product portfolio, and leaned in heavily to some major partnerships with cloud providers, as well as receiving a strategic investment from Snowflake earlier this year. So it's a really interesting story that we're thrilled to be sharing with you. Thanks for watching and I hope you enjoy the program. >>Last year, the Cube Covered Data Citizens Collibra's customer event. And the premise that we put forth prior to that event was that despite all the innovation that's gone on over the last decade or more with data, you know, starting with the Hado movement, we had data lakes, we'd spark the ascendancy of programming languages like Python, the introduction of frameworks like TensorFlow, the rise of ai, low code, no code, et cetera. Businesses still find it's too difficult to get more value from their data initiatives. And we said at the time, you know, maybe it's time to rethink data innovation. While a lot of the effort has been focused on, you know, more efficiently storing and processing data, perhaps more energy needs to go into thinking about the people and the process side of the equation, meaning making it easier for domain experts to both gain insights for data, trust the data, and begin to use that data in new ways, fueling data, products, monetization and insights data citizens 2022 is back and we're pleased to have Felix Van Dema, who is the founder and CEO of Collibra. He's on the cube or excited to have you, Felix. Good to see you again. >>Likewise Dave. Thanks for having me again. >>You bet. All right, we're gonna get the update from Felix on the current data landscape, how he sees it, why data intelligence is more important now than ever and get current on what Collibra has been up to over the past year and what's changed since Data Citizens 2021. And we may even touch on some of the product news. So Felix, we're living in a very different world today with businesses and consumers. They're struggling with things like supply chains, uncertain economic trends, and we're not just snapping back to the 2010s. That's clear, and that's really true as well in the world of data. So what's different in your mind, in the data landscape of the 2020s from the previous decade, and what challenges does that bring for your customers? >>Yeah, absolutely. And, and I think you said it well, Dave, and and the intro that that rising complexity and fragmentation in the broader data landscape, that hasn't gotten any better over the last couple of years. When when we talk to our customers, that level of fragmentation, the complexity, how do we find data that we can trust, that we know we can use has only gotten kinda more, more difficult. So that trend that's continuing, I think what is changing is that trend has become much more acute. Well, the other thing we've seen over the last couple of years is that the level of scrutiny that organizations are under respect to data, as data becomes more mission critical, as data becomes more impactful than important, the level of scrutiny with respect to privacy, security, regulatory compliance, as only increasing as well, which again, is really difficult in this environment of continuous innovation, continuous change, continuous growing complexity and fragmentation. >>So it's become much more acute. And, and to your earlier point, we do live in a different world and and the the past couple of years we could probably just kind of brute for it, right? We could focus on, on the top line. There was enough kind of investments to be, to be had. I think nowadays organizations are focused or are, are, are, are, are, are in a very different environment where there's much more focus on cost control, productivity, efficiency, How do we truly get value from that data? So again, I think it just another incentive for organization to now truly look at data and to scale it data, not just from a a technology and infrastructure perspective, but how do you actually scale data from an organizational perspective, right? You said at the the people and process, how do we do that at scale? And that's only, only only becoming much more important. And we do believe that the, the economic environment that we find ourselves in today is gonna be catalyst for organizations to really dig out more seriously if, if, if, if you will, than they maybe have in the have in the best. >>You know, I don't know when you guys founded Collibra, if, if you had a sense as to how complicated it was gonna get, but you've been on a mission to really address these problems from the beginning. How would you describe your, your, your mission and what are you doing to address these challenges? >>Yeah, absolutely. We, we started Colli in 2008. So in some sense and the, the last kind of financial crisis, and that was really the, the start of Colli where we found product market fit, working with large finance institutions to help them cope with the increasing compliance requirements that they were faced with because of the, of the financial crisis and kind of here we are again in a very different environment, of course 15 years, almost 15 years later. But data only becoming more important. But our mission to deliver trusted data for every user, every use case and across every source, frankly, has only become more important. So what has been an incredible journey over the last 14, 15 years, I think we're still relatively early in our mission to again, be able to provide everyone, and that's why we call it data citizens. We truly believe that everyone in the organization should be able to use trusted data in an easy, easy matter. That mission is is only becoming more important, more relevant. We definitely have a lot more work ahead of us because we are still relatively early in that, in that journey. >>Well, that's interesting because, you know, in my observation it takes seven to 10 years to actually build a company and then the fact that you're still in the early days is kind of interesting. I mean, you, Collibra's had a good 12 months or so since we last spoke at Data Citizens. Give us the latest update on your business. What do people need to know about your, your current momentum? >>Yeah, absolutely. Again, there's, there's a lot of tail organizations that are only maturing the data practices and we've seen it kind of transform or, or, or influence a lot of our business growth that we've seen, broader adoption of the platform. We work at some of the largest organizations in the world where it's Adobe, Heineken, Bank of America, and many more. We have now over 600 enterprise customers, all industry leaders and every single vertical. So it's, it's really exciting to see that and continue to partner with those organizations. On the partnership side, again, a lot of momentum in the org in, in the, in the markets with some of the cloud partners like Google, Amazon, Snowflake, data bricks and, and others, right? As those kind of new modern data infrastructures, modern data architectures that are definitely all moving to the cloud, a great opportunity for us, our partners and of course our customers to help them kind of transition to the cloud even faster. >>And so we see a lot of excitement and momentum there within an acquisition about 18 months ago around data quality, data observability, which we believe is an enormous opportunity. Of course, data quality isn't new, but I think there's a lot of reasons why we're so excited about quality and observability now. One is around leveraging ai, machine learning, again to drive more automation. And the second is that those data pipelines that are now being created in the cloud, in these modern data architecture arch architectures, they've become mission critical. They've become real time. And so monitoring, observing those data pipelines continuously has become absolutely critical so that they're really excited about about that as well. And on the organizational side, I'm sure you've heard a term around kind of data mesh, something that's gaining a lot of momentum, rightfully so. It's really the type of governance that we always believe. Then federated focused on domains, giving a lot of ownership to different teams. I think that's the way to scale data organizations. And so that aligns really well with our vision and, and from a product perspective, we've seen a lot of momentum with our customers there as well. >>Yeah, you know, a couple things there. I mean, the acquisition of i l dq, you know, Kirk Hasselbeck and, and their team, it's interesting, you know, the whole data quality used to be this back office function and, and really confined to highly regulated industries. It's come to the front office, it's top of mind for chief data officers, data mesh. You mentioned you guys are a connective tissue for all these different nodes on the data mesh. That's key. And of course we see you at all the shows. You're, you're a critical part of many ecosystems and you're developing your own ecosystem. So let's chat a little bit about the, the products. We're gonna go deeper in into products later on at, at Data Citizens 22, but we know you're debuting some, some new innovations, you know, whether it's, you know, the, the the under the covers in security, sort of making data more accessible for people just dealing with workflows and processes as you talked about earlier. Tell us a little bit about what you're introducing. >>Yeah, absolutely. We're super excited, a ton of innovation. And if we think about the big theme and like, like I said, we're still relatively early in this, in this journey towards kind of that mission of data intelligence that really bolts and compelling mission, either customers are still start, are just starting on that, on that journey. We wanna make it as easy as possible for the, for our organization to actually get started because we know that's important that they do. And for our organization and customers that have been with us for some time, there's still a tremendous amount of opportunity to kind of expand the platform further. And again, to make it easier for really to, to accomplish that mission and vision around that data citizen that everyone has access to trustworthy data in a very easy, easy way. So that's really the theme of a lot of the innovation that we're driving. >>A lot of kind of ease of adoption, ease of use, but also then how do we make sure that lio becomes this kind of mission critical enterprise platform from a security performance architecture scale supportability that we're truly able to deliver that kind of an enterprise mission critical platform. And so that's the big theme from an innovation perspective, From a product perspective, a lot of new innovation that we're really excited about. A couple of highlights. One is around data marketplace. Again, a lot of our customers have plans in that direction, how to make it easy. How do we make, how do we make available to true kind of shopping experience that anybody in your organization can, in a very easy search first way, find the right data product, find the right dataset, that data can then consume usage analytics. How do you, how do we help organizations drive adoption, tell them where they're working really well and where they have opportunities homepages again to, to make things easy for, for people, for anyone in your organization to kind of get started with ppia, you mentioned workflow designer, again, we have a very powerful enterprise platform. >>One of our key differentiators is the ability to really drive a lot of automation through workflows. And now we provided a new low code, no code kind of workflow designer experience. So, so really customers can take it to the next level. There's a lot more new product around K Bear Protect, which in partnership with Snowflake, which has been a strategic investor in kib, focused on how do we make access governance easier? How do we, how do we, how are we able to make sure that as you move to the cloud, things like access management, masking around sensitive data, PII data is managed as much more effective, effective rate, really excited about that product. There's more around data quality. Again, how do we, how do we get that deployed as easily and quickly and widely as we can? Moving that to the cloud has been a big part of our strategy. >>So we launch more data quality cloud product as well as making use of those, those native compute capabilities in platforms like Snowflake, Data, Bricks, Google, Amazon, and others. And so we are bettering a capability, a capability that we call push down. So actually pushing down the computer and data quality, the monitoring into the underlying platform, which again, from a scale performance and ease of use perspective is gonna make a massive difference. And then more broadly, we, we talked a little bit about the ecosystem. Again, integrations, we talk about being able to connect to every source. Integrations are absolutely critical and we're really excited to deliver new integrations with Snowflake, Azure and Google Cloud storage as well. So there's a lot coming out. The, the team has been work at work really hard and we are really, really excited about what we are coming, what we're bringing to markets. >>Yeah, a lot going on there. I wonder if you could give us your, your closing thoughts. I mean, you, you talked about, you know, the marketplace, you know, you think about data mesh, you think of data as product, one of the key principles you think about monetization. This is really different than what we've been used to in data, which is just getting the technology to work has been been so hard. So how do you see sort of the future and, you know, give us the, your closing thoughts please? >>Yeah, absolutely. And I, and I think we we're really at this pivotal moment, and I think you said it well. We, we all know the constraint and the challenges with data, how to actually do data at scale. And while we've seen a ton of innovation on the infrastructure side, we fundamentally believe that just getting a faster database is important, but it's not gonna fully solve the challenges and truly kind of deliver on the opportunity. And that's why now is really the time to deliver this data intelligence vision, this data intelligence platform. We are still early, making it as easy as we can. It's kind of, of our, it's our mission. And so I'm really, really excited to see what we, what we are gonna, how the marks gonna evolve over the next, next few quarters and years. I think the trend is clearly there when we talk about data mesh, this kind of federated approach folks on data products is just another signal that we believe that a lot of our organization are now at the time. >>The understanding need to go beyond just the technology. I really, really think about how do we actually scale data as a business function, just like we've done with it, with, with hr, with, with sales and marketing, with finance. That's how we need to think about data. I think now is the time given the economic environment that we are in much more focus on control, much more focused on productivity efficiency and now's the time. We need to look beyond just the technology and infrastructure to think of how to scale data, how to manage data at scale. >>Yeah, it's a new era. The next 10 years of data won't be like the last, as I always say. Felix, thanks so much and good luck in, in San Diego. I know you're gonna crush it out there. >>Thank you Dave. >>Yeah, it's a great spot for an in-person event and, and of course the content post event is gonna be available@collibra.com and you can of course catch the cube coverage@thecube.net and all the news@siliconangle.com. This is Dave Valante for the cube, your leader in enterprise and emerging tech coverage. >>Hi, I'm Jay from Collibra's Data Office. Today I want to talk to you about Collibra's data intelligence cloud. We often say Collibra is a single system of engagement for all of your data. Now, when I say data, I mean data in the broadest sense of the word, including reference and metadata. Think of metrics, reports, APIs, systems, policies, and even business processes that produce or consume data. Now, the beauty of this platform is that it ensures all of your users have an easy way to find, understand, trust, and access data. But how do you get started? Well, here are seven steps to help you get going. One, start with the data. What's data intelligence? Without data leverage the Collibra data catalog to automatically profile and classify your enterprise data wherever that data lives, databases, data lakes or data warehouses, whether on the cloud or on premise. >>Two, you'll then wanna organize the data and you'll do that with data communities. This can be by department, find a business or functional team, however your organization organizes work and accountability. And for that you'll establish community owners, communities, make it easy for people to navigate through the platform, find the data and will help create a sense of belonging for users. An important and related side note here, we find it's typical in many organizations that data is thought of is just an asset and IT and data offices are viewed as the owners of it and who are really the central teams performing analytics as a service provider to the enterprise. We believe data is more than an asset, it's a true product that can be converted to value. And that also means establishing business ownership of data where that strategy and ROI come together with subject matter expertise. >>Okay, three. Next, back to those communities there, the data owners should explain and define their data, not just the tables and columns, but also the related business terms, metrics and KPIs. These objects we call these assets are typically organized into business glossaries and data dictionaries. I definitely recommend starting with the topics that are most important to the business. Four, those steps that enable you and your users to have some fun with it. Linking everything together builds your knowledge graph and also known as a metadata graph by linking or relating these assets together. For example, a data set to a KPI to a report now enables your users to see what we call the lineage diagram that visualizes where the data in your dashboards actually came from and what the data means and who's responsible for it. Speaking of which, here's five. Leverage the calibra trusted business reporting solution on the marketplace, which comes with workflows for those owners to certify their reports, KPIs, and data sets. >>This helps them force their trust in their data. Six, easy to navigate dashboards or landing pages right in your platform for your company's business processes are the most effective way for everyone to better understand and take action on data. Here's a pro tip, use the dashboard design kit on the marketplace to help you build compelling dashboards. Finally, seven, promote the value of this to your users and be sure to schedule enablement office hours and new employee onboarding sessions to get folks excited about what you've built and implemented. Better yet, invite all of those community and data owners to these sessions so that they can show off the value that they've created. Those are my seven tips to get going with Collibra. I hope these have been useful. For more information, be sure to visit collibra.com. >>Welcome to the Cube's coverage of Data Citizens 2022 Collibra's customer event. My name is Dave Valante. With us is Kirk Hasselbeck, who's the vice president of Data Quality of Collibra Kirk, good to see you. Welcome. >>Thanks for having me, Dave. Excited to be here. >>You bet. Okay, we're gonna discuss data quality observability. It's a hot trend right now. You founded a data quality company, OWL dq, and it was acquired by Collibra last year. Congratulations. And now you lead data quality at Collibra. So we're hearing a lot about data quality right now. Why is it such a priority? Take us through your thoughts on that. >>Yeah, absolutely. It's, it's definitely exciting times for data quality, which you're right, has been around for a long time. So why now and why is it so much more exciting than it used to be? I think it's a bit stale, but we all know that companies use more data than ever before and the variety has changed and the volume has grown. And, and while I think that remains true, there are a couple other hidden factors at play that everyone's so interested in as, as to why this is becoming so important now. And, and I guess you could kind of break this down simply and think about if Dave, you and I were gonna build, you know, a new healthcare application and monitor the heartbeat of individuals, imagine if we get that wrong, you know, what the ramifications could be, what, what those incidents would look like, or maybe better yet, we try to build a, a new trading algorithm with a crossover strategy where the 50 day crosses the, the 10 day average. >>And imagine if the data underlying the inputs to that is incorrect. We will probably have major financial ramifications in that sense. So, you know, it kind of starts there where everybody's realizing that we're all data companies and if we are using bad data, we're likely making incorrect business decisions. But I think there's kind of two other things at play. You know, I, I bought a car not too long ago and my dad called and said, How many cylinders does it have? And I realized in that moment, you know, I might have failed him because, cause I didn't know. And, and I used to ask those types of questions about any lock brakes and cylinders and, and you know, if it's manual or, or automatic and, and I realized I now just buy a car that I hope works. And it's so complicated with all the computer chips, I, I really don't know that much about it. >>And, and that's what's happening with data. We're just loading so much of it. And it's so complex that the way companies consume them in the IT function is that they bring in a lot of data and then they syndicate it out to the business. And it turns out that the, the individuals loading and consuming all of this data for the company actually may not know that much about the data itself, and that's not even their job anymore. So we'll talk more about that in a minute, but that's really what's setting the foreground for this observability play and why everybody's so interested. It, it's because we're becoming less close to the intricacies of the data and we just expect it to always be there and be correct. >>You know, the other thing too about data quality, and for years we did the MIT CDO IQ event, we didn't do it last year, Covid messed everything up. But the observation I would make there thoughts is, is it data quality? Used to be information quality used to be this back office function, and then it became sort of front office with financial services and government and healthcare, these highly regulated industries. And then the whole chief data officer thing happened and people were realizing, well, they sort of flipped the bit from sort of a data as a, a risk to data as a, as an asset. And now as we say, we're gonna talk about observability. And so it's really become front and center just the whole quality issue because data's so fundamental, hasn't it? >>Yeah, absolutely. I mean, let's imagine we pull up our phones right now and I go to my, my favorite stock ticker app and I check out the NASDAQ market cap. I really have no idea if that's the correct number. I know it's a number, it looks large, it's in a numeric field. And, and that's kind of what's going on. There's, there's so many numbers and they're coming from all of these different sources and data providers and they're getting consumed and passed along. But there isn't really a way to tactically put controls on every number and metric across every field we plan to monitor, but with the scale that we've achieved in early days, even before calibra. And what's been so exciting is we have these types of observation techniques, these data monitors that can actually track past performance of every field at scale. And why that's so interesting and why I think the CDO is, is listening right intently nowadays to this topic is, so maybe we could surface all of these problems with the right solution of data observability and with the right scale and then just be alerted on breaking trends. So we're sort of shifting away from this world of must write a condition and then when that condition breaks, that was always known as a break record. But what about breaking trends and root cause analysis? And is it possible to do that, you know, with less human intervention? And so I think most people are seeing now that it's going to have to be a software tool and a computer system. It's, it's not ever going to be based on one or two domain experts anymore. >>So, So how does data observability relate to data quality? Are they sort of two sides of the same coin? Are they, are they cousins? What's your perspective on that? >>Yeah, it's, it's super interesting. It's an emerging market. So the language is changing a lot of the topic and areas changing the way that I like to say it or break it down because the, the lingo is constantly moving is, you know, as a target on this space is really breaking records versus breaking trends. And I could write a condition when this thing happens, it's wrong and when it doesn't it's correct. Or I could look for a trend and I'll give you a good example. You know, everybody's talking about fresh data and stale data and, and why would that matter? Well, if your data never arrived or only part of it arrived or didn't arrive on time, it's likely stale and there will not be a condition that you could write that would show you all the good in the bads. That was kind of your, your traditional approach of data quality break records. But your modern day approach is you lost a significant portion of your data, or it did not arrive on time to make that decision accurately on time. And that's a hidden concern. Some people call this freshness, we call it stale data, but it all points to the same idea of the thing that you're observing may not be a data quality condition anymore. It may be a breakdown in the data pipeline. And with thousands of data pipelines in play for every company out there there, there's more than a couple of these happening every day. >>So what's the Collibra angle on all this stuff made the acquisition, you got data quality observability coming together, you guys have a lot of expertise in, in this area, but you hear providence of data, you just talked about, you know, stale data, you know, the, the whole trend toward real time. How is Calibra approaching the problem and what's unique about your approach? >>Well, I think where we're fortunate is with our background, myself and team, we sort of lived this problem for a long time, you know, in, in the Wall Street days about a decade ago. And we saw it from many different angles. And what we came up with before it was called data observability or reliability was basically the, the underpinnings of that. So we're a little bit ahead of the curve there when most people evaluate our solution, it's more advanced than some of the observation techniques that that currently exist. But we've also always covered data quality and we believe that people want to know more, they need more insights, and they want to see break records and breaking trends together so they can correlate the root cause. And we hear that all the time. I have so many things going wrong, just show me the big picture, help me find the thing that if I were to fix it today would make the most impact. So we're really focused on root cause analysis, business impact, connecting it with lineage and catalog metadata. And as that grows, you can actually achieve total data governance at this point with the acquisition of what was a Lineage company years ago, and then my company Ldq now Collibra, Data quality Collibra may be the best positioned for total data governance and intelligence in the space. >>Well, you mentioned financial services a couple of times and some examples, remember the flash crash in 2010. Nobody had any idea what that was, you know, they just said, Oh, it's a glitch, you know, so they didn't understand the root cause of it. So this is a really interesting topic to me. So we know at Data Citizens 22 that you're announcing, you gotta announce new products, right? You're yearly event what's, what's new. Give us a sense as to what products are coming out, but specifically around data quality and observability. >>Absolutely. There's this, you know, there's always a next thing on the forefront. And the one right now is these hyperscalers in the cloud. So you have databases like Snowflake and Big Query and Data Bricks is Delta Lake and SQL Pushdown. And ultimately what that means is a lot of people are storing in loading data even faster in a SaaS like model. And we've started to hook in to these databases. And while we've always worked with the the same databases in the past, they're supported today we're doing something called Native Database pushdown, where the entire compute and data activity happens in the database. And why that is so interesting and powerful now is everyone's concerned with something called Egress. Did your, my data that I've spent all this time and money with my security team securing ever leave my hands, did it ever leave my secure VPC as they call it? >>And with these native integrations that we're building and about to unveil, here's kind of a sneak peek for, for next week at Data Citizens. We're now doing all compute and data operations in databases like Snowflake. And what that means is with no install and no configuration, you could log into the Collibra data quality app and have all of your data quality running inside the database that you've probably already picked as your your go forward team selection secured database of choice. So we're really excited about that. And I think if you look at the whole landscape of network cost, egress, cost, data storage and compute, what people are realizing is it's extremely efficient to do it in the way that we're about to release here next week. >>So this is interesting because what you just described, you know, you mentioned Snowflake, you mentioned Google, Oh actually you mentioned yeah, data bricks. You know, Snowflake has the data cloud. If you put everything in the data cloud, okay, you're cool, but then Google's got the open data cloud. If you heard, you know, Google next and now data bricks doesn't call it the data cloud, but they have like the open source data cloud. So you have all these different approaches and there's really no way up until now I'm, I'm hearing to, to really understand the relationships between all those and have confidence across, you know, it's like Jak Dani, you should just be a note on the mesh. And I don't care if it's a data warehouse or a data lake or where it comes from, but it's a point on that mesh and I need tooling to be able to have confidence that my data is governed and has the proper lineage, providence. And, and, and that's what you're bringing to the table, Is that right? Did I get that right? >>Yeah, that's right. And it's, for us, it's, it's not that we haven't been working with those great cloud databases, but it's the fact that we can send them the instructions now, we can send them the, the operating ability to crunch all of the calculations, the governance, the quality, and get the answers. And what that's doing, it's basically zero network costs, zero egress cost, zero latency of time. And so when you were to log into Big Query tomorrow using our tool or like, or say Snowflake for example, you have instant data quality metrics, instant profiling, instant lineage and access privacy controls, things of that nature that just become less onerous. What we're seeing is there's so much technology out there, just like all of the major brands that you mentioned, but how do we make it easier? The future is about less clicks, faster time to value, faster scale, and eventually lower cost. And, and we think that this positions us to be the leader there. >>I love this example because, you know, Barry talks about, wow, the cloud guys are gonna own the world and, and of course now we're seeing that the ecosystem is finding so much white space to add value, connect across cloud. Sometimes we call it super cloud and so, or inter clouding. All right, Kirk, give us your, your final thoughts and on on the trends that we've talked about and Data Citizens 22. >>Absolutely. Well, I think, you know, one big trend is discovery and classification. Seeing that across the board, people used to know it was a zip code and nowadays with the amount of data that's out there, they wanna know where everything is, where their sensitive data is. If it's redundant, tell me everything inside of three to five seconds. And with that comes, they want to know in all of these hyperscale databases how fast they can get controls and insights out of their tools. So I think we're gonna see more one click solutions, more SAS based solutions and solutions that hopefully prove faster time to value on, on all of these modern cloud platforms. >>Excellent. All right, Kurt Hasselbeck, thanks so much for coming on the Cube and previewing Data Citizens 22. Appreciate it. >>Thanks for having me, Dave. >>You're welcome. Right, and thank you for watching. Keep it right there for more coverage from the Cube. Welcome to the Cube's virtual Coverage of Data Citizens 2022. My name is Dave Valante and I'm here with Laura Sellers, who's the Chief Product Officer at Collibra, the host of Data Citizens. Laura, welcome. Good to see you. >>Thank you. Nice to be here. >>Yeah, your keynote at Data Citizens this year focused on, you know, your mission to drive ease of use and scale. Now when I think about historically fast access to the right data at the right time in a form that's really easily consumable, it's been kind of challenging, especially for business users. Can can you explain to our audience why this matters so much and what's actually different today in the data ecosystem to make this a reality? >>Yeah, definitely. So I think what we really need and what I hear from customers every single day is that we need a new approach to data management and our product teams. What inspired me to come to Calibra a little bit a over a year ago was really the fact that they're very focused on bringing trusted data to more users across more sources for more use cases. And so as we look at what we're announcing with these innovations of ease of use and scale, it's really about making teams more productive in getting started with and the ability to manage data across the entire organization. So we've been very focused on richer experiences, a broader ecosystem of partners, as well as a platform that delivers performance, scale and security that our users and teams need and demand. So as we look at, Oh, go ahead. >>I was gonna say, you know, when I look back at like the last 10 years, it was all about getting the technology to work and it was just so complicated. But, but please carry on. I'd love to hear more about this. >>Yeah, I, I really, you know, Collibra is a system of engagement for data and we really are working on bringing that entire system of engagement to life for everyone to leverage here and now. So what we're announcing from our ease of use side of the world is first our data marketplace. This is the ability for all users to discover and access data quickly and easily shop for it, if you will. The next thing that we're also introducing is the new homepage. It's really about the ability to drive adoption and have users find data more quickly. And then the two more areas of the ease of use side of the world is our world of usage analytics. And one of the big pushes and passions we have at Collibra is to help with this data driven culture that all companies are trying to create. And also helping with data literacy, with something like usage analytics, it's really about driving adoption of the CLE platform, understanding what's working, who's accessing it, what's not. And then finally we're also introducing what's called workflow designer. And we love our workflows at Libra, it's a big differentiator to be able to automate business processes. The designer is really about a way for more people to be able to create those workflows, collaborate on those workflow flows, as well as people to be able to easily interact with them. So a lot of exciting things when it comes to ease of use to make it easier for all users to find data. >>Y yes, there's definitely a lot to unpack there. I I, you know, you mentioned this idea of, of of, of shopping for the data. That's interesting to me. Why this analogy, metaphor or analogy, I always get those confused. I let's go with analogy. Why is it so important to data consumers? >>I think when you look at the world of data, and I talked about this system of engagement, it's really about making it more accessible to the masses. And what users are used to is a shopping experience like your Amazon, if you will. And so having a consumer grade experience where users can quickly go in and find the data, trust that data, understand where the data's coming from, and then be able to quickly access it, is the idea of being able to shop for it, just making it as simple as possible and really speeding the time to value for any of the business analysts, data analysts out there. >>Yeah, I think when you, you, you see a lot of discussion about rethinking data architectures, putting data in the hands of the users and business people, decentralized data and of course that's awesome. I love that. But of course then you have to have self-service infrastructure and you have to have governance. And those are really challenging. And I think so many organizations, they're facing adoption challenges, you know, when it comes to enabling teams generally, especially domain experts to adopt new data technologies, you know, like the, the tech comes fast and furious. You got all these open source projects and get really confusing. Of course it risks security, governance and all that good stuff. You got all this jargon. So where do you see, you know, the friction in adopting new data technologies? What's your point of view and how can organizations overcome these challenges? >>You're, you're dead on. There's so much technology and there's so much to stay on top of, which is part of the friction, right? It's just being able to stay ahead of, of and understand all the technologies that are coming. You also look at as there's so many more sources of data and people are migrating data to the cloud and they're migrating to new sources. Where the friction comes is really that ability to understand where the data came from, where it's moving to, and then also to be able to put the access controls on top of it. So people are only getting access to the data that they should be getting access to. So one of the other things we're announcing with, with all of the innovations that are coming is what we're doing around performance and scale. So with all of the data movement, with all of the data that's out there, the first thing we're launching in the world of performance and scale is our world of data quality. >>It's something that Collibra has been working on for the past year and a half, but we're launching the ability to have data quality in the cloud. So it's currently an on-premise offering, but we'll now be able to carry that over into the cloud for us to manage that way. We're also introducing the ability to push down data quality into Snowflake. So this is, again, one of those challenges is making sure that that data that you have is d is is high quality as you move forward. And so really another, we're just reducing friction. You already have Snowflake stood up. It's not another machine for you to manage, it's just push down capabilities into Snowflake to be able to track that quality. Another thing that we're launching with that is what we call Collibra Protect. And this is that ability for users to be able to ingest metadata, understand where the PII data is, and then set policies up on top of it. So very quickly be able to set policies and have them enforced at the data level. So anybody in the organization is only getting access to the data they should have access to. >>Here's Topica data quality is interesting. It's something that I've followed for a number of years. It used to be a back office function, you know, and really confined only to highly regulated industries like financial services and healthcare and government. You know, you look back over a decade ago, you didn't have this worry about personal information, g gdpr, and, you know, California Consumer Privacy Act all becomes, becomes so much important. The cloud is really changed things in terms of performance and scale and of course partnering for, for, with Snowflake it's all about sharing data and monetization, anything but a back office function. So it was kind of smart that you guys were early on and of course attracting them and as a, as an investor as well was very strong validation. What can you tell us about the nature of the relationship with Snowflake and specifically inter interested in sort of joint engineering or, and product innovation efforts, you know, beyond the standard go to market stuff? >>Definitely. So you mentioned there were a strategic investor in Calibra about a year ago. A little less than that I guess. We've been working with them though for over a year really tightly with their product and engineering teams to make sure that Collibra is adding real value. Our unified platform is touching pieces of our unified platform or touching all pieces of Snowflake. And when I say that, what I mean is we're first, you know, able to ingest data with Snowflake, which, which has always existed. We're able to profile and classify that data we're announcing with Calibra Protect this week that you're now able to create those policies on top of Snowflake and have them enforce. So again, people can get more value out of their snowflake more quickly as far as time to value with, with our policies for all business users to be able to create. >>We're also announcing Snowflake Lineage 2.0. So this is the ability to take stored procedures in Snowflake and understand the lineage of where did the data come from, how was it transformed with within Snowflake as well as the data quality. Pushdown, as I mentioned, data quality, you brought it up. It is a new, it is a, a big industry push and you know, one of the things I think Gartner mentioned is people are losing up to $15 million without having great data quality. So this push down capability for Snowflake really is again, a big ease of use push for us at Collibra of that ability to, to push it into snowflake, take advantage of the data, the data source, and the engine that already lives there and get the right and make sure you have the right quality. >>I mean, the nice thing about Snowflake, if you play in the Snowflake sandbox, you, you, you, you can get sort of a, you know, high degree of confidence that the data sharing can be done in a safe way. Bringing, you know, Collibra into the, into the story allows me to have that data quality and, and that governance that I, that I need. You know, we've said many times on the cube that one of the notable differences in cloud this decade versus last decade, I mean ob there are obvious differences just in terms of scale and scope, but it's shaping up to be about the strength of the ecosystems. That's really a hallmark of these big cloud players. I mean they're, it's a key factor for innovating, accelerating product delivery, filling gaps in, in the hyperscale offerings cuz you got more stack, you know, mature stack capabilities and you know, it creates this flywheel momentum as we often say. But, so my question is, how do you work with the hyperscalers? Like whether it's AWS or Google, whomever, and what do you see as your role and what's the Collibra sweet spot? >>Yeah, definitely. So, you know, one of the things I mentioned early on is the broader ecosystem of partners is what it's all about. And so we have that strong partnership with Snowflake. We also are doing more with Google around, you know, GCP and kbra protect there, but also tighter data plex integration. So similar to what you've seen with our strategic moves around Snowflake and, and really covering the broad ecosystem of what Collibra can do on top of that data source. We're extending that to the world of Google as well and the world of data plex. We also have great partners in SI's Infosys is somebody we spoke with at the conference who's done a lot of great work with Levi's as they're really important to help people with their whole data strategy and driving that data driven culture and, and Collibra being the core of it. >>Hi Laura, we're gonna, we're gonna end it there, but I wonder if you could kind of put a bow on, you know, this year, the event your, your perspectives. So just give us your closing thoughts. >>Yeah, definitely. So I, I wanna say this is one of the biggest releases Collibra's ever had. Definitely the biggest one since I've been with the company a little over a year. We have all these great new product innovations coming to really drive the ease of use to make data more valuable for users everywhere and, and companies everywhere. And so it's all about everybody being able to easily find, understand, and trust and get access to that data going forward. >>Well congratulations on all the pro progress. It was great to have you on the cube first time I believe, and really appreciate you, you taking the time with us. >>Yes, thank you for your time. >>You're very welcome. Okay, you're watching the coverage of Data Citizens 2022 on the cube, your leader in enterprise and emerging tech coverage. >>So data modernization oftentimes means moving some of your storage and computer to the cloud where you get the benefit of scale and security and so on. But ultimately it doesn't take away the silos that you have. We have more locations, more tools and more processes with which we try to get value from this data. To do that at scale in an organization, people involved in this process, they have to understand each other. So you need to unite those people across those tools, processes, and systems with a shared language. When I say customer, do you understand the same thing as you hearing customer? Are we counting them in the same way so that shared language unites us and that gives the opportunity for the organization as a whole to get the maximum value out of their data assets and then they can democratize data so everyone can properly use that shared language to find, understand, and trust the data asset that's available. >>And that's where Collibra comes in. We provide a centralized system of engagement that works across all of those locations and combines all of those different user types across the whole business. At Collibra, we say United by data and that also means that we're united by data with our customers. So here is some data about some of our customers. There was the case of an online do it yourself platform who grew their revenue almost three times from a marketing campaign that provided the right product in the right hands of the right people. In other case that comes to mind is from a financial services organization who saved over 800 K every year because they were able to reuse the same data in different kinds of reports and before there was spread out over different tools and processes and silos, and now the platform brought them together so they realized, oh, we're actually using the same data, let's find a way to make this more efficient. And the last example that comes to mind is that of a large home loan, home mortgage, mortgage loan provider where they have a very complex landscape, a very complex architecture legacy in the cloud, et cetera. And they're using our software, they're using our platform to unite all the people and those processes and tools to get a common view of data to manage their compliance at scale. >>Hey everyone, I'm Lisa Martin covering Data Citizens 22, brought to you by Collibra. This next conversation is gonna focus on the importance of data culture. One of our Cube alumni is back, Stan Christians is Collibra's co-founder and it's Chief Data citizens. Stan, it's great to have you back on the cube. >>Hey Lisa, nice to be. >>So we're gonna be talking about the importance of data culture, data intelligence, maturity, all those great things. When we think about the data revolution that every business is going through, you know, it's so much more than technology innovation. It also really re requires cultural transformation, community transformation. Those are challenging for customers to undertake. Talk to us about what you mean by data citizenship and the role that creating a data culture plays in that journey. >>Right. So as you know, our event is called Data Citizens because we believe that in the end, a data citizen is anyone who uses data to do their job. And we believe that today's organizations, you have a lot of people, most of the employees in an organization are somehow gonna to be a data citizen, right? So you need to make sure that these people are aware of it. You need that. People have skills and competencies to do with data what necessary and that's on, all right? So what does it mean to have a good data culture? It means that if you're building a beautiful dashboard to try and convince your boss, we need to make this decision that your boss is also open to and able to interpret, you know, the data presented in dashboard to actually make that decision and take that action. Right? >>And once you have that why to the organization, that's when you have a good data culture. Now that's continuous effort for most organizations because they're always moving, somehow they're hiring new people and it has to be continuous effort because we've seen that on the hand. Organizations continue challenged their data sources and where all the data is flowing, right? Which in itself creates a lot of risk. But also on the other set hand of the equation, you have the benefit. You know, you might look at regulatory drivers like, we have to do this, right? But it's, it's much better right now to consider the competitive drivers, for example, and we did an IDC study earlier this year, quite interesting. I can recommend anyone to it. And one of the conclusions they found as they surveyed over a thousand people across organizations worldwide is that the ones who are higher in maturity. >>So the, the organizations that really look at data as an asset, look at data as a product and actively try to be better at it, don't have three times as good a business outcome as the ones who are lower on the maturity scale, right? So you can say, ok, I'm doing this, you know, data culture for everyone, awakening them up as data citizens. I'm doing this for competitive reasons, I'm doing this re reasons you're trying to bring both of those together and the ones that get data intelligence right, are successful and competitive. That's, and that's what we're seeing out there in the market. >>Absolutely. We know that just generally stand right, the organizations that are, are really creating a, a data culture and enabling everybody within the organization to become data citizens are, We know that in theory they're more competitive, they're more successful. But the IDC study that you just mentioned demonstrates they're three times more successful and competitive than their peers. Talk about how Collibra advises customers to create that community, that culture of data when it might be challenging for an organization to adapt culturally. >>Of course, of course it's difficult for an organization to adapt but it's also necessary, as you just said, imagine that, you know, you're a modern day organization, laptops, what have you, you're not using those, right? Or you know, you're delivering them throughout organization, but not enabling your colleagues to actually do something with that asset. Same thing as through with data today, right? If you're not properly using the data asset and competitors are, they're gonna to get more advantage. So as to how you get this done, establish this. There's angles to look at, Lisa. So one angle is obviously the leadership whereby whoever is the boss of data in the organization, you typically have multiple bosses there, like achieve data officers. Sometimes there's, there's multiple, but they may have a different title, right? So I'm just gonna summarize it as a data leader for a second. >>So whoever that is, they need to make sure that there's a clear vision, a clear strategy for data. And that strategy needs to include the monetization aspect. How are you going to get value from data? Yes. Now that's one part because then you can leadership in the organization and also the business value. And that's important. Cause those people, their job in essence really is to make everyone in the organization think about data as an asset. And I think that's the second part of the equation of getting that right, is it's not enough to just have that leadership out there, but you also have to get the hearts and minds of the data champions across the organization. You, I really have to win them over. And if you have those two combined and obviously a good technology to, you know, connect those people and have them execute on their responsibilities such as a data intelligence platform like s then the in place to really start upgrading that culture inch by inch if you'll, >>Yes, I like that. The recipe for success. So you are the co-founder of Collibra. You've worn many different hats along this journey. Now you're building Collibra's own data office. I like how before we went live, we were talking about Calibra is drinking its own champagne. I always loved to hear stories about that. You're speaking at Data Citizens 2022. Talk to us about how you are building a data culture within Collibra and what maybe some of the specific projects are that Collibra's data office is working on. >>Yes, and it is indeed data citizens. There are a ton of speaks here, are very excited. You know, we have Barb from m MIT speaking about data monetization. We have Dilla at the last minute. So really exciting agen agenda. Can't wait to get back out there essentially. So over the years at, we've doing this since two and eight, so a good years and I think we have another decade of work ahead in the market, just to be very clear. Data is here to stick around as are we. And myself, you know, when you start a company, we were for people in a, if you, so everybody's wearing all sorts of hat at time. But over the years I've run, you know, presales that sales partnerships, product cetera. And as our company got a little bit biggish, we're now thousand two. Something like people in the company. >>I believe systems and processes become a lot important. So we said you CBRA isn't the size our customers we're getting there in of organization structure, process systems, et cetera. So we said it's really time for us to put our money where is and to our own data office, which is what we were seeing customers', organizations worldwide. And they organizations have HR units, they have a finance unit and over time they'll all have a department if you'll, that is responsible somehow for the data. So we said, ok, let's try to set an examples that other people can take away with it, right? Can take away from it. So we set up a data strategy, we started building data products, took care of the data infrastructure. That's sort of good stuff. And in doing all of that, ISA exactly as you said, we said, okay, we need to also use our product and our own practices and from that use, learn how we can make the product better, learn how we make, can make the practice better and share that learning with all the, and on, on the Monday mornings, we sometimes refer to eating our dog foods on Friday evenings. >>We referred to that drinking our own champagne. I like it. So we, we had a, we had the driver to do this. You know, there's a clear business reason. So we involved, we included that in the data strategy and that's a little bit of our origin. Now how, how do we organize this? We have three pillars, and by no means is this a template that everyone should, this is just the organization that works at our company, but it can serve as an inspiration. So we have a pillar, which is data science. The data product builders, if you'll or the people who help the business build data products. We have the data engineers who help keep the lights on for that data platform to make sure that the products, the data products can run, the data can flow and you know, the quality can be checked. >>And then we have a data intelligence or data governance builders where we have those data governance, data intelligence stakeholders who help the business as a sort of data partner to the business stakeholders. So that's how we've organized it. And then we started following the CBRA approach, which is, well, what are the challenges that our business stakeholders have in hr, finance, sales, marketing all over? And how can data help overcome those challenges? And from those use cases, we then just started to build a map and started execution use of the use case. And a important ones are very simple. We them with our, our customers as well, people talking about the cata, right? The catalog for the data scientists to know what's in their data lake, for example, and for the people in and privacy. So they have their process registry and they can see how the data flows. >>So that's a starting place and that turns into a marketplace so that if new analysts and data citizens join kbra, they immediately have a place to go to, to look at, see, ok, what data is out there for me as an analyst or a data scientist or whatever to do my job, right? So they can immediately get access data. And another one that we is around trusted business. We're seeing that since, you know, self-service BI allowed everyone to make beautiful dashboards, you know, pie, pie charts. I always, my pet pee is the pie chart because I love buy and you shouldn't always be using pie charts. But essentially there's become proliferation of those reports. And now executives don't really know, okay, should I trust this report or that report the reporting on the same thing. But the numbers seem different, right? So that's why we have trusted this reporting. So we know if a, the dashboard, a data product essentially is built, we not that all the right steps are being followed and that whoever is consuming that can be quite confident in the result either, Right. And that silver browser, right? Absolutely >>Decay. >>Exactly. Yes, >>Absolutely. Talk a little bit about some of the, the key performance indicators that you're using to measure the success of the data office. What are some of those KPIs? >>KPIs and measuring is a big topic in the, in the data chief data officer profession, I would say, and again, it always varies with to your organization, but there's a few that we use that might be of interest. Use those pillars, right? And we have metrics across those pillars. So for example, a pillar on the data engineering side is gonna be more related to that uptime, right? Are the, is the data platform up and running? Are the data products up and running? Is the quality in them good enough? Is it going up? Is it going down? What's the usage? But also, and especially if you're in the cloud and if consumption's a big thing, you have metrics around cost, for example, right? So that's one set of examples. Another one is around the data sciences and products. Are people using them? Are they getting value from it? >>Can we calculate that value in ay perspective, right? Yeah. So that we can to the rest of the business continue to say we're tracking all those numbers and those numbers indicate that value is generated and how much value estimated in that region. And then you have some data intelligence, data governance metrics, which is, for example, you have a number of domains in a data mesh. People talk about being the owner of a data domain, for example, like product or, or customer. So how many of those domains do you have covered? How many of them are already part of the program? How many of them have owners assigned? How well are these owners organized, executing on their responsibilities? How many tickets are open closed? How many data products are built according to process? And so and so forth. So these are an set of examples of, of KPIs. There's a, there's a lot more, but hopefully those can already inspire the audience. >>Absolutely. So we've, we've talked about the rise cheap data offices, it's only accelerating. You mentioned this is like a 10 year journey. So if you were to look into a crystal ball, what do you see in terms of the maturation of data offices over the next decade? >>So we, we've seen indeed the, the role sort of grow up, I think in, in thousand 10 there may have been like 10 achieve data officers or something. Gartner has exact numbers on them, but then they grew, you know, industries and the number is estimated to be about 20,000 right now. Wow. And they evolved in a sort of stack of competencies, defensive data strategy, because the first chief data officers were more regulatory driven, offensive data strategy support for the digital program. And now all about data products, right? So as a data leader, you now need all of those competences and need to include them in, in your strategy. >>How is that going to evolve for the next couple of years? I wish I had one of those balls, right? But essentially I think for the next couple of years there's gonna be a lot of people, you know, still moving along with those four levels of the stack. A lot of people I see are still in version one and version two of the chief data. So you'll see over the years that's gonna evolve more digital and more data products. So for next years, my, my prediction is it's all products because it's an immediate link between data and, and the essentially, right? Right. So that's gonna be important and quite likely a new, some new things will be added on, which nobody can predict yet. But we'll see those pop up in a few years. I think there's gonna be a continued challenge for the chief officer role to become a real executive role as opposed to, you know, somebody who claims that they're executive, but then they're not, right? >>So the real reporting level into the board, into the CEO for example, will continue to be a challenging point. But the ones who do get that done will be the ones that are successful and the ones who get that will the ones that do it on the basis of data monetization, right? Connecting value to the data and making that value clear to all the data citizens in the organization, right? And in that sense, they'll need to have both, you know, technical audiences and non-technical audiences aligned of course. And they'll need to focus on adoption. Again, it's not enough to just have your data office be involved in this. It's really important that you're waking up data citizens across the organization and you make everyone in the organization think about data as an asset. >>Absolutely. Because there's so much value that can be extracted. Organizations really strategically build that data office and democratize access across all those data citizens. Stan, this is an exciting arena. We're definitely gonna keep our eyes on this. Sounds like a lot of evolution and maturation coming from the data office perspective. From the data citizen perspective. And as the data show that you mentioned in that IDC study, you mentioned Gartner as well, organizations have so much more likelihood of being successful and being competitive. So we're gonna watch this space. Stan, thank you so much for joining me on the cube at Data Citizens 22. We appreciate it. >>Thanks for having me over >>From Data Citizens 22, I'm Lisa Martin, you're watching The Cube, the leader in live tech coverage. >>Okay, this concludes our coverage of Data Citizens 2022, brought to you by Collibra. Remember, all these videos are available on demand@thecube.net. And don't forget to check out silicon angle.com for all the news and wiki bod.com for our weekly breaking analysis series where we cover many data topics and share survey research from our partner ETR Enterprise Technology Research. If you want more information on the products announced at Data Citizens, go to collibra.com. There are tons of resources there. You'll find analyst reports, product demos. It's really worthwhile to check those out. Thanks for watching our program and digging into Data Citizens 2022 on the Cube, your leader in enterprise and emerging tech coverage. We'll see you soon.

>> Announcer: From theCUBE studios in Palo Alto and Boston, it's the theCUBE, covering Empowering the Autonomous Enterprise, brought to you by Oracle Consulting. >> Welcome to this special digital presentation, where we're tracking the rebirth of Oracle Consulting, and my name is Dave Vellante and we're here with Aaron Millstone who's the Senior Vice President of Oracle Consulting. Aaron thanks for coming on good to talk to you. >> Dave, appreciate you having me, and I like the introduction of the rebirth of Oracle Consulting. >> Well it really is. I mean you guys have gone from staff augmentation to being a much more of a strategic partner, and we're going to talk about that, but I want to start with this theme that you have about empowering the autonomous enterprise. Sounds good, you know, nice little marketing tagline, but give us what's behind that, put some meat on the bone. >> Sure, right, so what we define as the autonomous enterprise is really using artificial intelligence, using machine learning and using it to cognitively understand your actual data and process you're using for your enterprise, and then really embedding that into everything you're doing as a company, and using it to do both drive optimization of costs and increasing revenue. And I know that's a lot of kind of consultey speak, so we tend to think about, and we've been talking about in terms of what we call tri-modal IT, and this is probably the most exciting space that I've really thought through with my team as we've built up a new consulting business, you pointed out. But this is really about pivoting away from the systems of record and the systems of interaction, and really building up the systems of intelligence capabilities that we see all enterprises needing to invest in heavily, if they're not already investing there currently. >> Well I want to talk about a couple of things here. One is that notion of lowering cost or increasing revenue, and you're right, people say oh yeah that's consultant speak but a good consultant digs in and starts peeling the onion. Well how do you actually make money? Where are the inefficiencies in your business? And that's really what you're talking about, and that's what every business wants to know, right? That's the end game, but the how-to is really what separates the good consultants from the bad. >> Correct, correct. And again, we're on this journey now. You mentioned it right, I'm two years into Oracle Consulting. Myself, I spent 23 plus years at Accenture, where I was a Managing Director with them, and part of their North American leadership team. When I came over to Oracle Consulting, we did, we pivoted from what you call staff augmentation business to a basic set of offerings which were things that's you'd recognize, right, migration services of workloads to cloud, or integration of security work, or you know, even paths for SaaS augmentation that we would give, but pretty basic services. We're now pivoting again into two areas, infrastructure-led transformation, which is really our bold costs take-out play, as you just said, and sort of good consultants know how to do that. and really what that is, is we're going and looking at companies that still have traditional data centers or maybe they've got some things on clouds and some things still in traditional data centers, and we're coming in and we're saying there's a business case here, that looks at your total cost of ownership, and we think we can take out between 40 and 65% of your run-rate costs, and that's everything from facilities, fire suppression systems, through to the actual compute costs, through to the labor that's required to do the physical or hands-on activities in the data center, right. So we have that sort of capability, and we're pushing customers hard in that space at the moment, and then driving that into a secondary conversation which says and by the way, all these savings, you kind of have two choices, right. You can pocket the savings, obviously, or we would propose that you go into what we're calling the autonomous enterprise phase, and really building up your artificial intelligence machine learning capability with centralized capabilities, centralized data, versus letting every line of business, every department do it on their own. >> So let me ask you, so that makes sense, but why your cloud? You were sort of later entrants into cloud. So where does cloud fit into this? How do you respond when customers say, "Yeah but you guys were late coming to cloud"? >> Yeah we are definitely late coming to cloud. There's no two ways about it. I mean, what we've got is we have what we call a generation two cloud. And I jokingly tell customers that we have a late mover advantage, and that late mover advantage basically means that we've looked at what the first generation clouds have done, and quite frankly they're great at what they do, they're fierce competitors, they're tough to compete with, they've got a lot of mind share, but they fundamentally were about targeting consumers, who are targeting enterprise collaboration tools. So if you want cat videos, if you want to watch humorous videos that people have filmed and posted on social media, those are great clouds for that stuff, but if you want really mission-critical enterprise cloud workloads, that's were we come into play. And so when you start to look at really the key differentiators in our cloud, at least this is how I describe it to our customers right, so we look at sort of three layers. We have an autonomous capability, in both our operating system and our database. What that basically means is that we have machine learning and artificial intelligence that's driving the key administrative activities in our cloud. We then have then our Exadata platform. So Exadata for us is a secret weapon. We think that it is a core differentiator in our products. And so, Exadata, for those watching that don't necessarily know what it is, right, so Exadata emerged out of the Sun acquisition that Oracle did. It is purpose-built hardware that is engineered for our software products, specifically our databases, and now we've taken that concept and moved it into our cloud. And so customers can come in and take very intensive enterprise mission-critical workloads, run them straight in our cloud. And then, when we look at the last point, it's product security, where, again, we have total segmentation of our security layers from the customer workloads, right. So again, we've taken the concepts that first generation cloud providers have implemented, and they've scaled it globally so it's really tough for them to walk back from it, it's a huge investment. And we're, have gone into a generation two cloud and quite frankly, I think this is the frontier that everyone's racing to kind of crack. >> So we've got to wrap, but I wanted to close on sort of the, again we've talked about good consultants and good consultants have continuous improvement mindset. They got a north star that they never really get to and that keeps moving, because you've got to keep innovating, you've got to keep disrupting yourself. So maybe you could end by talking about some of the things you're watching, some of the milestones you want to hit, and some of that transformation that you want to keep going. How are you going to achieve that? >> Yeah we'll get some of it when we hit the Deloitte segment too right, but we're definitely, we've moved from, we've definitely moved from the staff augmentation to basic offerings. We're now beyond that. We're starting to sell the infrastructure-led transformation plays. What's exciting to me about that with our customers is, Oracle's a big complex enterprise, as you'd expect with a company that has a tremendous amount of technology. We're now bringing holistic approaches to our customers, saying "Let us help you optimize everything "and let's look at your data center". Let's not look at a narrow slice, let's not look at sysadmins and DBAs. We're looking at things comprehensively. So moving there has been a pretty big milestone for us to hit. We've started to get some good momentum with our customers. Our next milestone is really going to be taking that autonomous enterprise and blowing it out. We're in use case and incubation period right now with that, but again, we've got some, I would argue we have the best talent in the world right now that thinks about this stuff, and not just thinks about it from a computer technology standpoint, but thinks about how to actually to make it effective for the business. And so once we get some of those motions going, the use case for the autonomous enterprise, that's artificial intelligence driven. It should have a continuous pace of change, and it's going to start to evolve in areas that, quite frankly, we can't even predict yet, but we're excited to see where it leads. >> Well Aaron, thanks for spending some time with us. I am very excited to talk about that sort of collision course between your deep tech capabilities as Oracle as a product company, and the global SI, Deloitte, we're going to bring in those guys in a moment. So thanks very much for taking us through the transformation, and great job, good luck. >> Thank you, appreciate it. >> All right, and thank you everybody for watching. Keep it right there, we'll be back with more coverage of Oracle's transformation right after this short break. You're watching theCUBE. (gentle electronic music)

(upbeat music) >> Everybody, welcome to this special digital presentation where we're tracking the rebirth of Oracle Consulting. And my name is Dave Vellante, and we're here with Aaron Millstone who's the senior vice president of Oracle Consulting. Aaron, thanks for coming on, good to talk to you. >> Dave, appreciate you having me and I like the introduction of a rebirth of Oracle Consulting. >> Well, it really is I mean, you know, you guys have gone from staff augmentation to being much more of a strategic partner and we're going to talk about that. But I want to start with this theme that you have about empowering the autonomous enterprise. Sounds good, you know, nice little marketing tagline. But give us what's behind that, put some meat on the bone? >> Sure, so you know, what we define as autonomous enterprise is really using artificial intelligence, using machine learning and using it to cognitively understand your actual data and processes you're using for your enterprise. And then really embedding that into everything you're doing as a company, and using it to be both drive optimization and costs, and increasing revenue. And I know that's a lot of kind of consulting speak. So, we tend to think about what we've been talking about in terms of what we call tri-modal IT. This is probably the most exciting space that I've really thought through with my team, as we build up a new consulting business, you pull it out, but this is really about pivoting away from the systems of record and the systems of interaction, and really building up the systems of intelligence capabilities that we see all enterprises needing to invest in heavily, if they're not already investing there already. >> Well, I want to talk about a couple of things there. You know, one is that notion of lowering cost or increasing revenue and you're right people say, Oh, yeah, that's consultancy people, but a good consultant digs in and starts peeling the onion. Well, how do you actually make money? You know, where are the inefficiencies in your business? And that's really what you're talking about, and that's what every business wants to know, right? That's the end game, but the how to is really what separates the good consultants from the pack. >> Right right. And we're, you know, again, we're on this journey now, we've been, you mentioned it right. I'm two years into Oracle Consulting. Myself, I spent 23 plus years at Accenture, where I was a managing director with them and part of their North American leadership team. When I came over to Oracle consulting we did, we pivoted from what you called staff augmentation business to a basic set of offerings, which were things that you recognize right migration, services of workloads to cloud or integration or security work or even, step paths for SAS augmentation that we would do, but you know, pretty basic services. We're now pivoting again into sort of two areas infrastructure and transformation, which is really our bold costs take out play, as you just said, and sort of good consultants know how to do that. And really what that is, is we're going and looking at companies that still have traditional data centers, or maybe they've got some things on clouds, and something's still in traditional data centers. And we're coming in, and we're saying there's a business case here, that looks at your total cost of ownership. And we think we can take out between 40 and 65% of your run rate costs, and that's everything from, facilities, fire suppression systems, through to the actual compute cost, through to the labor that's required to do the physical hands on activities in the data center. So, we have that sort of capability and we're pushing customers hard in that space at the moment, and then driving that into a secondary conversation system and by the way, with all these savings, you kind of have to choices. You can pocket the savings, obviously or we would propose that you go into what we're calling the autonomous enterprise space, and really building up your artificial intelligence machine learning capability with centralized capabilities, centralized data, versus letting every line of business, every department do it on their own. >> Now, the other thing a good consultant does is they make the initiative self funding, and that's a win win you keep getting paid, the customer makes money, that's a good thing. But I like the idea, you're starting with the obvious business case of cost, and I think I heard you really attacking OPPEX, labor is obviously big component of that, but it's not just labor, and then you transition if they don't pocket the gain to a gain sharing going forward to look for new revenue. Did I get that I get that right? >> Yeah, you actually got that right. And actually, what I'll tell you too, is I think the labor piece, again, you know, I came from Accenture, Accenture is big outsourcing company, big technology consulting, big strategy consulting. You know, I went in for years and did pitches on outsourcing arrangements which were fundamentally lower cost bodies running in a more effective way. What we're finding or what I'm finding with customer conversations over the last two years at Oracle has been actually I think, data centers are not, there's nothing competitively advantageous about having a data center if you're a company, there is a lot of advantageous. There's an advantage to having cloud and what we're seeing is that companies that might have outsourced their data center are just the lowest cost provider are now considering insourcing or co-sourcing as they pivot the cloud. So the funny thing is actually labor savings is not the big driver of that 40 to 65%, that plays a role of course, that's how you get to the 65%. But even go into the 40% you can get there by insourcing your labor and bringing them in house and recognizing that the speed at which you can operate on your cloud gives you a competitive advantage. >> So this requires a whole new skill set for Oracle, you mentioned, you came in from Accenture where I talked to another number of other folks in Oracle's North America Consulting Operation that came from, brand name firms, we're going to be talking to Deloitte we have and will continue. So there I know, a big part of you talk about the skills transformation that you've affected inside of Oracle Consulting. >> Sure, yeah I mean, it started when I showed up. It was primarily a staff augmentation business in our commercial space in particular, you know, if you need a DBA, here's a DBA. If you need a SAS admin, here's a SAS admin. Here's the hourly rates and quite frankly, very, very talented group of people, very talented, but focused on doing, you know, sort of nuts and bolts level work, very deep work on the Oracle technology stack, but also weren't particularly cloud certified. So we started by focusing on getting the team certified in our cloud products, invested a ton of hours, thousands and thousands of hours in training. It takes you know, we're doing something like six months investment initially to get people up and certified on multiple cloud products that Oracle is selling. And then right from there, we started putting together our basic offerings, again moved from staff augmentation to saying, look, would you like to move a workload. To move a workload is going to cost a fixed price, whatever that is 100, $200,000 move away from rate card conversations with augmentation. And we shifted the commercial contracts that had payments based on outcomes so they don't move successfully, there's no payment. And so you know that was really the focus. >> I'm going to come back to this notion of gain sharing and particularly focus on the revenue side for a moment. You mentioned a what I'll call a buzzword tri-modal IT and a buzzword because Gartner kind of with bimodal IT popularized that concept. And I think part of the problem that people had with bimodal IT was kind of had the legacy systems of record and then you had all the new cool stuff, the big data and you know now AI and systems of engagement and so forth. And everybody wanted to go to the ladder and run away from the former. But now, if I understand tri-model IT, you're talking about bringing machine intelligence to both of those spheres such that people can stay current, stay relevant and add new value to their organization. >> Yeah, that's exactly it. And we're trying to bring it to both but we're trying to make it its own sphere, independent of the other two. So, again, as we looked at this consulting evolution, I didn't come over to Oracle and Oracle is not interested in us, creating a consulting business, that's a me too consulting business that kind of looks like whatever everyone else is doing. So the goal really was okay. So if we started with sort of staff augmentation, and you know, really Oracle's legacy, a system of record stuff, we sell big back office systems, we have mission critical databases. Like it's the clunky stuff that has to work, but really at the end of the day, that's our heritage, going over to the systems of interaction which is, where the bimodal IT really came in from Gartner. That's a pretty saturated place, so again, coming from the background, I had a consulting, I looked at all the eight design agencies that were out there that were all selling digital, and we looked at the digital sales tactics going on, we're like, well, that's pretty saturated, it's not really a smart place for us to go make a lot of headway into. And so we looked and said, well really, the next layer, the next evolution of IT is this third sphere systems of intelligence. And really, since Oracle is, our heritage is mission critical and data, fundamentally, the logical step for us was to go okay, systems intelligence are powered by data, and they serve artificial intelligence as the primary consumer. So again, our thought process was you have a system of record which is process centric and really geared towards the CFO or a head of HR, you have systems of interaction, which is really geared towards the users, it's trying to make business frictionless. Those users can be consumers, they can be employees, whomever. And then systems intelligence is around artificial intelligence is the primary consumer of it. I mean really pivoting to that, and then making that something that is pervasive and structurally place across both those other two spheres, really felt like where we should be differentiating. When I brought in the talent rate that we looked to bring in, we were getting kind of affirmation that, yeah, the best talent in the market was starting to see this trend and so we kind of knew we were onto something there. >> Yeah, I mean, that makes a lot of sense, because as you as you point out, some of those new workloads, many of them are very consumer oriented, that's kind of you know, not your wheelhouse. I mean, that's your customers are, selling to consumers, but Oracle's B2B, hardcore data mission critical. But let me ask you, to that make sets, but by your cloud, you were sort of a later entrant into cloud. So where does cloud fit into this? How do you respond to when customers say, yeah, but you know, you guys were late on the cloud. >> Yeah, we are definitely late coming to cloud, like there's no two ways about it. I mean, what we've got is we have what we call a Generation 2 Cloud. And I jokingly tell customers that we have a late mover advantage. And that late mover advantage basically means that we've looked at what the first generation clouds have done. And quite frankly, they're great at what they do, they're fierce competitors, they're tough to compete with, they've got a lot of mindshare, but they fundamentally were about targeting consumers, or targeting enterprise collaboration tools, so if you want cat videos, if you want to watch humorous videos that people filmed and posted on social media, those are great clouds for that stuff. But if you want really mission critical enterprise cloud workloads, that's where we come into play. And so when you start to look at really the key differentiators in our cloud and through out, at least this is how I describe it to customers. So, we look at sort of three layers, we have an autonomous capability both on our operating system and our database. What that basically means is that we have machine learning and artificial intelligence that's driving the key, administrative activities in our cloud, we then have our Exadata platform. So Exadata for us is a secret weapon, we think that it is a differentiator in our products. And so, Exadata for those watching that doesn't know what it is, so Exadata emerged out of the Sun acquisition that Oracle did. It is purpose built hardware that is engineered for our software products, specifically our databases. And now we've taken that concept and moved it into our cloud and so customers can come in and take very intensive enterprise, mission critical workloads, run them straight in our cloud. And then, when we look at the last point, it's probably security where, again, we have total segmentation of our security layers from the customer workloads. So again, we've taken the concepts that first generation cloud providers have implemented, and they've scaled it globally. So it's really tough for them to walk back on it, it's a huge investment and we're now gone into a Generation 2 Cloud and quite frankly, I think that's what this is the frontier that everyone's racing to kind of grab. >> You know, we actually in our community, talk to a lot of Exadata customers and they get very intense, they do some really hardcore things with with Exadata. To me, the key to your cloud strategy, and specifically Exadata is you've got the same exact infrastructure, control plane, data plane, software, either on prem or in the cloud. So that's your same same narrative. But the real key, new key anyway is what autonomous, tell me if you agree with this. What autonomous gives you a scale, because as you say, you're related to cloud, you're not a hyper scalar in that sense, you're not selling just, race to the bottom infrastructure as a service. You're bringing applications and mission critical applications, so eponymous gives you the ability to scale and compete more effectively with some of those other, earlier movers. You buy that? >> Yeah, absolutely. So scaling and scaling in terms of, what has been historically human activities, when I say human activities, we're not replacing the humans, we're making some of the human activities that were highly repetitive way more efficient. So easy example I can give you is patching. Like security(mumbles) bases are very time consuming, I've talked to customers as recently as a couple weeks ago, that are three years behind on their patching. And when I look at that, it's you're like, why wouldn't you consider autonomous, they have their board of directors and their auditors are actually now demanding that they do something different about their patching problems. And they're talking about, man months, people months of trying to roll out this patching, and they're worried about breaking stuff, and they're worried about human error. Like when you look at something like autonomous, that patching would take place, pretty much instantaneous with no downtime. And we've seen it in our own cloud and our own services internally and we're able to patch, thousands and thousands of cores very, very quickly. >> So we got to wrap but I wanted to close on sort of the, I mean, again, we talked about good consultants and good consultants have continuous improvement mindset. They got a North star that they really never get through and that keeps moving because you got to keep innovating, you got to keep disrupting yourself, so maybe you could end by sort of talking about some of the things you're watching, some of the milestones you want to hit and some of that transformation that you want to keep going. How are you going to achieve that? >> Yeah, and it will skip some of it, when we hit the Deloitte segment too, but like we're definitely we've moved from, we've definitely move from the staff augmentation to basic offerings. We're now beyond that we're starting to sell the infrastructure lead transformation plays. What's exciting to me about that with our customers is, you know, Oracle's a big complex enterprise, as you'd expect with a company that has a tremendous amount of technology. We're now bringing holistic approaches to our customer say, let us help you optimize everything end to end, let's look at your data center, let's not look at a narrow slice, let's not look at just SAS admins and DBAs, we're looking at things comprehensively. So moving there has been a pretty big milestone for us to hit, we've started to get some good momentum with our customers. Our next milestone is really going to be taking that autonomous enterprise and blowing it out. We're in use case and incubation period right now with that, but again we've got some, I would argue we have the best talent in the world right now that thinks about this stuff and not just thinks about it from a pure technology standpoint, but thinks about how to actually make it effective for the business. And so once we get some of those motions going, like the use case for the autonomous enterprise that's artificial intelligence driven, it should have a continuous pace of change, and it's going to start to evolve in areas that you know, quite frankly, we can't even predict yet. But we're excited to see where it leads. >> Alright, thanks for spending some time with us. I am very excited to talk about that sort of collision course between your deep tech capabilities as Oracle as a product company and this, the Global SI, Deloitte, we're going to bring in those guys in a moment. So thanks very much for taking us through the transformation and great job, good luck. >> Thank you, appreciate it. >> All right, and thank you, everybody for watching. Keep right there, we'll be back with more coverage of Oracle's transformation. Right after the short break, you're watching the CUBE. (upbeat music)

>>live from Barcelona, Spain. It's the Cube covering Cisco Live 2020 right to you by Cisco and its ecosystem partners. >>Welcome back to the Cisco Live 2020 show in Barcelona, Spain. It's the Cube's live coverage. Four days of action. I'm John Furrier with my co host, Dave Vellante. Stew Minimum is in the house. We've been really interview all the thought. Leads all the action here in the DEV. Net zone of Cisco. We're here with Susie Wee, who's the senior vice president, chief technology officer and general manager of Cisco's DEV. Net and C X ecosystem success. Susie, great to see you again, Thanks to you. With our third year we've been we've been watching the growth of definite explode and definite create a separate event for developers. Great to see you. >>Great to see you. Great to be here. >>So how does it feel to be on a wave of success? You've had quite an impact in the industry, and I think the biggest story that's going on in the industry is the role of developers. You guys have embraced that four years ago, brought it all together and really just been marching to the cadence of just humble training, education and programming, all the Cisco products enabling what it looks like to be the future of Cisco. >>Yeah, I mean, it's it's humbling, t So you know what's been really great? It's really all about our community. And, you know, I mean, you guys have jumped in, been with us on this journey. You've seen it like all around us in terms of how it's progressed. But what's interesting is that, you know, networkers the software developers, the Dev Ops pros that people who are coming into definite really progressing, they're getting to the next level. And then we have more and more new people coming in. And what happens is the technology keeps advancing right. So networking, security going toe intent based networking, multi domain. How do you integrate these things? Cognitive collaboration. I o t an edge, you know, edge computing. As all of this comes together, you get to a really interesting place. But what happens is we have to think about I t department networking departments like how do people use this to their advantage? Right. So there's actually users of people who install and run these things and how do they make that available and actually get a business advantage out of that infrastructure? That's what this is all about. >>And the big scene. Wendy on the opening keynote, kicked off before David came on. She had a slide that I thought encapsulate what I think the future of all business and you guys have been on and on and on, a reference that it was people in communities, business model and business operations almost like a three legged stool. You've been on this because your team Michael was on the Cube just now said people have been in their careers on Cisco. But Cisco is betting the business on the people, that ecosystem, it's developers. CC III is the certifications. This dynamic of the role of the people is critical, >>and they're driving >>the change >>it is. And you know what was tremendous about Cisco's business model and how Cisco was founded. So this was pre me, you know, and it's just the brilliance of the early folks is like Cisco made this router, you know? It was a little start up. It was like five people, right? And then it started flying off the shelf in the mid eighties in late eighties when the Internet started taking off, and then the way they scale that out was by growing the community, they didn't say We're going to hire people around the world to install these networks. We're gonna create a community of professionals who can go around and install these networks. And then we're going to create a partner ecosystem of partners who are going to build businesses around this, installing networks for customers. And so really, Cisco very early on, learn that we had to be very customer focused and build with an ecosystem of partners. And then we created Cisco Certification Program, and that started to take the people who are getting trained to do networking and give them certifications. And then they were able to get jobs in customers and partners and build their careers. And so now we move that to today, and we're continuing with that philosophy and doubling down. It's about them, except there's a shift in technology. So the network has changed. It's not the same old network like now. There's new capabilities that require software. It requires dev ops. It requires applications to hit the infrastructure it requires. I T and Infrastructure to solve business problems. But we need to bring the people along and doing that, and that is absolutely what we've been about. >>I said in my breaking analysis there were there were many things that helped Cisco rise with the three things like pointed out where the bet on I p, the M and A and then I was too narrow. I liked how you describe it as the community, but really talking about the Army of trained engineers that were advocates. And you're extending that to the partner ecosystem. What's interesting about watching this rise over the faster uses? Not only transformation of Cisco from hardware to software and now even business transformation is you see, I t go from a cost center to a profit center, but you're sort of following that track. I don't know if you're leading it are following it sort of incompetent what's going on. And, >>you know, I would say >>that we're doing both because, uh, obviously we're listening to customers and partners all the time to see what do you need? So we're listening, and that would make us leading as we're sorry. Following is >>we're >>listening and yet we're creating technology to enable them to do these new things right? So there's a reason that you can think about the network to be solving business problems. It's because we made the networking programmable and based on software. If we didn't make it software, it would still be running the old way. And it wouldn't be able to play in a Dev ops loop or be automated or anything there. So I would say that it's very combined. But Cisco takes a holistic approach right back there. We have an I T managers forum where there are people who are trying to say, Hey, you know, I've been leading technology teams in I T. But I need to learn how to talk to the business, right? So there's a transformation that needs to happen, which is okay, The technologist networkers I t folks themselves need to learn about software. But then also, these folks and their managers need to be able to talk to the business and think differently. So take some design thinking. Think about what are the business stakeholders problems where customers problems, how can I make my technology work for them? So we really have a lot going on Teoh building the kind of success of our ecosystem. >>Yeah, it's interesting you mentioned technology shift, and that's causing a lot of change is actually how people are certified business models. And it's interesting. When we were chatting years ago, Dev. Ops was actually out there. The hyper scales around you saw it evolving was pretty clear to a lot of the insiders. That's Dev Ops. Infrastructure is code. Then you kicked on something where programmable networks I heard this week, and this is kind of again goes to the next level and kind of connect the dots. Biz Dev. Ops. So the AB dynamics guys, look at this as OK. So this agile attitude yes, has been on for a while. Could you comment? I think >>a lot of people that >>are looking at Cisco trying to understand its evolution where it's gonna go >>yeah, >>is rooted in years ago. A shift in thinking, yes, and it's an agile It's a dev ops mindset, >>yes, but >>the Dev ops notion from whether it's pure Dev ops, cloud native or Dev ops or Biz Dev ops >>for what's next? So this is a It's been around for a while. You just share your Yeah, Absolutely. So >>I have a slide and we don't show slides here, which is a good thing. But it was called it the hamburger slide. So the hamburger slide, where there would be infrastructure and the applications. And then there's this other layer appear business, you know. And basically, what happens is the infrastructure became programmable, so as opposed to the infrastructure and the applications being separate, the I T teams did the infrastructure of the app Dev did. The businesses did the APS. Then now that the infrastructure's family can get into a dev ops workflow. So for cloud applications, the APS and the infrastructure can really mix. And now the network is programmable. So there's Net Dev ops. And it's not just compute that can get into Dev ops. But you know, the network can too. But then, now that business layer can flow into this. And so what happens is once again, you could say that cloud enables business, right? And so, if you know, a business is trying to say, how do I compete like a retail store? How my completing with a cloud competitors. Well, you have to embrace it. Take your traditional infrastructure, your customer data your stores, but then mix that with cloud offerings. That's a huge transformation that needs to happen. But now there's even more capabilities. As you're saying, Hey, I'm like a coffee shop and I'm rolling out all of these stores. How do I make sure my business applications get there? How do I get customer intelligence and business intelligence together so my workers can serve my customers with the right knowledge and information they need so you can actually use the infrastructure and APS as an advantage in how you serve your business? And you wouldn't even be able to do those things if you didn't know about the technology. So I would say that there's like a workforce trend where technology is enabling business and it can grow your business in different ways. But we need to make sure that we can express that because the technologist doesn't usually talk in terms of the business. But that's where all the value >>on the application has always been. That point of business value in connection to the business when the APP is the infrastructure has been removed from that now that the infrastructure's becoming programmable. It's embedded into that application, and developers can now add value on top of it. I mean, the striking thing to me was just behind us, to seeing a number of your customers lining up to learn how to code in Python. And then I o t was off the charts. And I've always been saying that Look at the edge is going to be one by developers E. I think you really got that right. I'm curious as to why you think just really is the one company in a large, established player. That is, I think, figured it out that I've said that many have tried throwing money at the problem, reaching out to developers fallen flat. I mean, even very successful software companies were struggling. Why do you think Cisco has had successes? Is a culture is at the leverage of that certification and community that you talked about earlier? >>Yeah, it's while it's really hard to say, like one reason why, because these air tricky things, like so taking on a new business strategy, getting everybody aligned in a big company, even in a little company, is hard, but it takes like everybody pushing towards the direction and what happens is different. People get it at different times. So obviously with Dev net, we're trying to push something along. The CEO Chuck Robbins. He got it and he was pushing it. And then the businesses and product teams. Some of them had a P I first, and some of them did not. But now more and more on almost all of them do. Now. All the products have AP eyes and they're getting more AP I first and now what we're doing is aligning AP eyes across the portfolio. You need to get your sales teams to understand and to engage. Like the regions. We have people in Italy who are engaging with the Italian community. We have our seas around the world that are basically engaging the people in each of their countries to evangelize it in tow, work with customers and partners in their local language is using this material to get them on board. So, you know, when we started, Definite Way had different ways we could take it. No one defined a developer program for a company like Cisco before, like a networking company, but we actually didn't do it by saying, Oh, we're only gonna talk to application developers and ignore those old networkers We said we're going to make them core and bring them along and bring in the captives and bring them together. I wouldn't say we're gonna, like, forget about the old Cisco products We said we're gonna work with them as they add AP eyes and make that better. We're gonna ignore our sales guys and the ones that we're going to bring them along and make them our evangelists and advocates to work with the region. So we kind of use the whole fabric along with it and just I kind of gained. The community >>recognized the appetite for building, and some people are like, >>I'm going to jump in and give this a try because I think it's important and something like, I'm gonna wait and see and they're like, Oh, it's something now, Okay, now I'll jump in and we're like, >>That's right, >>you're totally We do a lot of Cuban. It is many different events here at Cisco over the years. It's interesting to see when people get in and you can see it when their eyes pop up. Oh, I get it. It is a progression of whether they're orientation, what their background is. But it seems to me the early people who click it on it is our systems thinkers. Most of the techies, they're systems systems, folks. Yeah, they see as a system not as one thing. Yes, As you said, it's not just absent infrastructure. So a lot of the system guys get it first. And then on the business side, they see it from more of the making money. So you see the impact of the application changing the business model. It's a retail app or whatever they get it. That that's gonna be the future. Yeah, it depends on where you're coming from. >>It does. It does. And what's interesting is to >>see how this community has evolved and actually, how we've evolved to be able to support people along the way. So as you remember, when you were first year, it was really some techies who realized they needed to learn something new. So is about learning about software and AP eyes. And then we evolved. It became about coding. So how do we use a definite automation exchange in code exchange to use a software based model to build community code around networking use cases because they wanted to use it and get it into use cases. And then now we have people are like, Okay, I'm doing it, I get it. But I can't get my business leaders to understand. So now we're actually helping them express the business case and create use cases that solve business problems more directly, so >>your access to customer success >>and customer success. So now explain that piece. What is that? How >>to be successful at training is everything >>customer direction. What is that piece? So s >>o me and my team were Cisco employees, and sometimes I mean, this doesn't get represented, but we move around the York, so you know, as different things change. And so there's a recent move where it has been in the engineering team. I've now moved into the customer experience organization. We're doing a transformation like a customer experience, customer success, transformation for Cisco and so you know, as we think about that. Well, first of all, Cisco's always been customer oriented, But what does this mean in a world of software in world of partners? ecosystems with the products and opportunities we have now. And so, as we're gearing towards this kind of customer success and customer experience model, is that, you know, they're trying to do a transformation, and it's actually very similar to what Dev Net has already done, which is specifically, let's see. So when you engage with a company on new technology, we can say Okay, come here to the DEV Net Zone and learn about the AP eyes, you know. But as you're working with a customer and you say, Hey, you know you're from the customer, let's go on this journey together. Did you know that we have AP eyes? Let's learn about AP eyes. >>And did you know that >>this product performs this function? But it also has AP eyes. So let's teach you about those. Then you learn a different aspect of the product that you might not have thought about before because you're like, Oh, it can be a platform and then you say, Hey, and you know you need to solve automation. This can be used to solve automation, and so then you're like, Oh, I'm thinking about automation, but how do I do it? so you can't have just one product. That's >>that's a progression that depends on what the customer's orientation is, whether environment looks like >>so it >>means, like start to evolve and think about their problem. Actually, their problem is automation. Their problem is not using this product right. They're trying to solve a bigger product and hopefully this is a bigger business problem or an automation problem. And this product is a piece of the puzzle into it. So we want to kind of engage in the full discussion from what is your need, an automation and then work backwards toe like, How can this product help? And so it's kind of like turning things upside down and ensuring the customer uses. And, you know, we understand their business problem. We're helping them solve it. And this is how these products can play a role in helping you achieve that >>in every business is looking at that from the corner office. They all want to drive automation into their business. They're looking at okay, if the economy turns out more automation, whether it's you know, you see an R P. A takeoff is the cloud is supporting that, Yeah, it's a big trend >>is huge, and it's, you know, and actually moving to an automation infrastructure. It's not like buy a new product and you've automated and you're done. It's actually very hard, and it requires an architectural shift. It means, like I'm going to start to build telemetry, analyze data and get insights from it. Well, if you don't have that implemented somewhere, then you need to architect for them. And then once you start building into that and seeing dashboards and then connecting that into other business APS, then you start to go further and further so every step along the way, we want to get them closer to an automation architecture. But that takes work, >>and it's cultural as well as people hear automation. If it well, that's my job and so >>little >>education. And then once they see it, Oh, you mean I could get rid of all these things I don't like to do, and I can do this instead. Then they really lean in and create new value. >>Yeah, So what we're getting at is this, like, really interesting. I'll call it a new technology trend of looking at kind of automation, plus Ai together, right? And so I've been talking about it out here in some places, which is now we've been talking about automation. We've been talking about AI. You look at these together. There's a set of people who are like, Let's think about what automation means. It could mean Oh my gosh, someone's going to take my job away. I don't need people anymore That would be called like autonomous. And there's some things that you do want to make autonomous and work themselves. But then you can also look at kind of assisting humans. Right? So assisting like, what are you trying to do? Roll out configurations across different places and get them set up where we can automate that and you can assist a human? And being able to do it on this next age is augmenting humans. What is there that a person really couldn't do that they can do now in a night? Example of that is, you know, you take a look at threat intelligence and security going around the world. Cisco has products around the world that are looking for security threats. You put those together, you can see a threat before it comes to a customer environment and say, Hey, we found this threat. We better shut it down over in your system to make sure you're blocked and protected from it. You've augmented human capability, you know, using automation and AI. >>You know, one of the things a lot of companies do is they focus on a big wave and they focus on it. They get on that new wave. Cisco's on a lot of different ways. You got I, O. T. And Security, which you were talking about. This kubernetes and Cloud native is like all these collaboration. They're all their own big waves coming. So I have to ask you because you've been so successful, definite and then a great leader in the industry with all your experience. What's your vision as this comes in? Because Cisco is that one of the benefits uniquely positioned with all the complexity, all the opportunities to the Dev ops, like across the board up and down the stack, these waves are coming. It's not just one. You have a focus on kubernetes. You got a focus on security. There's all these different big things that you guys are working on. What's your vision >>on how >>this all plays out >>like so while there's different, there's different things going on kubernetes and cloud. You know, we're doing networking. What's going on in I O. T and Edge Computing and the Future of Cognitive Collaboration and AI and ML, And you know all of this kind of thing a security I don't actually view them as separate. Actually view them is all part of a bigger system, right? They're part of a platform that's trying to solve a bigger problem, >>and the secret is AP ice. So it's actually a >>combination of architecture in AP eyes and how this works is a fabric together and you know there's benefit. Like if you're trying to do security, sure, you can use security products to do security. But why don't you also use network segmentation to do security, like literally segment out pieces of the network and, you know, data and APS that should not be talking to other places and use that for security? So, you know, I kind of view it is all working together towards a bigger architecture because you're using Ap eyes. You can start to put these things together and start to apply policies across these different domains. So this kind of whole new area, another new technology trend, is looking at multi domain opportunities and cross architecture. So that's really key >>in the data that you get out of that as well, right? Data and metadata that you can analyze and then act upon. Yes, Dr. Inside >>multi domain, multi clouds Having >>data models, right? Look at how do you take, you know, so that all these different systems are adding up to a everything you need to create data models that these different applications can kind of pour into >>that used to be locked inside of a box. Sitting in >>these types of application would have its own >>kind of model, But we're really all working towards the bigger thing in software that lets you down in >>the silicon is a great thing to get so looking One coming, Yes, moving from the box of the chip. Yeah, not a bad strategy. >>Super interesting. So, yeah, >>if you look at, you know, where are the bottlenecks in this? And this is where you need to rethink what your business strategy is. And it's just like you down in the optics down at that layer is where the big opportunities are. And if we can differentiate and provide value in that space, then that's what we've done. We >>were riffing the other night in the taxi came in I said, The day of Digital and digital, which is the Internet's all digital. Now the business model is the killer app, and we're just more of a provocative statement like, What are you trying to dio with that? What all this is? What's the purpose of all this? >>Yeah, I >>have a business model that actually works. >>It is, But it is, Yeah, >>and what's interesting about the business model? Also, to think about that? It's not just your own business model. It's again. That's where that's why I called our new group ecosystem success. It's what you do, you know. And there's this whole model of success, meaning you your customer, your supply chain up above you and then how you deliver. But it's east west now, too, right? It's like, How does your innovation work with your partner's innovation? Another area that and how did this all happen together? Like, how do you take trends in security and advances there and, you know, in workforce and people. And as you take a look at, you know, everything that's happening in cloud and then intersect so that we're all successful >>and it's enabled by what you're saying before automation and AI obviously supported by Cloud AP eyes and data across that system that you guys were talking >>about, I think that I think the bumper sticker for Cisco's Cisco connects businesses because that's really what you're doing. >>There we go way >>shut up for the 1st 500 >>Yes, yes, yes. So yes. So some of the big news over here is that well, in this >>world of where the infrastructure becomes programmable. So what Cisco's had a long time is Cisco's sort of certification program. So we have ccn a Cisco Certified Network Associates. Si Si n Pi's CC III is the expert level, and that's been an industry standard for the last 26 years, and people have job roles. They've gotten promotions, they get recognized, their certified for delivering quality, and what we've introduced is the definite certifications. So, in addition to the engineering certifications or the software certifications and Devon, it's kind of growing to the next level. By so far, everybody who's been in here has been into definite because of their hearts and because they knew they had to learn anything. But now we're giving them a certification so they can be recognized as their efforts, and we're expanding Cisco certification to cover it. Now. This represents the move of engineering plus software together in your I T teams and together for your technology teams and the new certifications. The definite set of Cisco Live February 24th the 1st 500 people to earn a definite certification. We're going to call the definite 500. And >>so they want to be the first >>ones who are really stepping forward in this new industrial shift towards combining engineering and software, making the world of the infrastructure talking to business and driving business happen. >>Well, we'd love to be First, get a list of >>thousands of people 500 seats that will take. We'll take the 501st 10,000 in the 1,000,000 I dive >>Heard Susie. Some Cisco VP's want to get into that 500. >>They yes, Gamification. >>Always a good strategy, Susie. Great to watch your successes with folks watching, seeing definitely come from an idea execution and now core to the business model's been quite an evolution. Congratulations. Always success. >>Thank you. And thank you for joining us on this journey. >>So we've been working together on it. >>We've learned a lot. It's been so much fun. We're in the DEV Net zone. I'm John Furrier Dave Vellante with Susie Wee, the chief of the definite team and the big zones gets bigger every year. And the cube's getting big air thanks to you and the team. Appreciate it is to keep more live coverage from Barcelona. Cisco live 2020 after this short break. >>Yeah, yeah, yeah.

>> from Cambridge, Massachusetts. It's the Cube covering M I T. Chief data officer and information quality Symposium 2019. Brought to you by Silicon Angle Media. >> Welcome back to Cambridge, Massachusetts. Everybody watching the Cuban leader live tech coverage. We here covering the M I t CDO conference M I t CEO Day to wrapping up. Bob Parr is here. He's a partner in principle at KPMG, and he's joined by Streetcar Krishna, who is the managing director of data science. Aye, aye. And innovation at KPMG. Gents, welcome to the Cube. Thank >> thank you. Let's start with your >> roles. So, Bob, where do you focus >> my focus? Ah, within KPMG, we've got three main business lines audit tax, an advisory. And so I'm the advisory chief date officer. So I'm more focused on how we use data competitively in the market. More the offense side of our focus. So, you know, how do we make sure that our teams have the data they need to deliver value? Uh, much as possible working concert with the enterprise? CDO uh, who's more focused on our infrastructure, Our standards, security, privacy and those >> you've focused on making KPMG better A >> supposed exactly clients. OK, >> I also have a second hat, and I also serve financial service is si Dios as well. So Okay, so >> get her out of a dual role. I got sales guys in >> streetcar. What was your role? >> Yeah, You know, I focus a lot on data science, artificial intelligence and overall innovation s o my reaction. I actually represent a centre of >> excellence within KPMG that focuses on the I machine learning natural language processing. And I work with Bob's Division to actually advance the data site off the store because all the eye needs data. And without data, there's no algorithms, So we're focusing a lot on How do we use a I to make data Better think about their equality. Think about data lineage. Think about all of the problems that data has. How can we make it better using algorithms? And I focused a lot on that working with Bob, But no, it's it's customers and internal. I mean, you know, I were a horizontal within the form, So we help customers. We help internal, we focus a lot on the market. >> So, Bob, you mentioned used data offensively. So 10 12 years ago, it was data was a liability. You had to get rid of it. Keep it no longer than you had to, because you're gonna get soon. So email archives came in and obviously thinks flipped after the big data. But so what do you What are you seeing in terms of that shift from From the defense data to the offensive? >> Yeah, and it's it's really you know, when you think about it and let me define sort of offense versus defense. Who on the defense side, historically, that's where most of CEOs have played. That's risk regulatory reporting, privacy, um, even litigation support those types of activities today. Uh, and really, until about a year and 1/2 ago, we really saw most CEOs still really anchored in that I run a forum with a number of studios and financial service is, and every year we get them together and asked him the same set of questions. This was the first year where they said that you know what my primary focus now is. Growth. It's bringing efficiency is trying to generate value on the offensive side. It's not like the regulatory work's going away, certainly in the face of some of the pending privacy regulation. But you know, it's It's a sign that the volume of use cases as the investments in their digital transformations are starting to kick out, as well as the volumes of data that are available. The raw material that's available to them in terms of third party data in terms of the the just the general volumes that that exist that are streaming into the organization and the overall literacy in the business units are creating this, this massive demand. And so they're having to >> respond because of getting a handle on the data they're actually finding. Word is, they're categorizing it there, there, >> yeah, organizing that. That is still still a challenge. Um, I think it's better with when you have a very narrow scope of critical data elements going back to the structure data that we're talking it with the regulatory reporting when you start to get into the three offense, the generating value, getting the customer experience, you know, really exploring. You know that side of it. There's there's a ton of new muscle that has to be built new muscle in terms of data quality, new muscle in terms of um, really more scalable operating model. I think that's a big issue right now with Si Dios is, you know, we've got ah, we're used to that limited swath of CDs and they've got Stewardship Network. That's very labor intensive. A lot of manual processes still, um, and and they have some good basic technology, but it's a lot of its rules based. And when you do you think about those how that constraints going to scale when you have all of this demand. You know, when you look at the customer experience analytics that they want to do when you look at, you know, just a I applied to things like operations. The demand on the focus there is is is gonna start to create a fundamental shift >> this week are one of things that I >> have scene, and maybe it's just my small observation space. But I wonder, if you could comment Is that seems like many CBO's air not directly involved in the aye aye initiatives. Clearly, the chief digital officer is involved, but the CDO zehr kind of, you know, in the background still, you see that? >> That's a fantastic question, and I think this is where we're seeing some off the cutting it change that is happening in the industry. And when Barbara presenter idea that we can often civilly look at data, this is what it is that studios for a long time have become more reactive in their roles. And that is that is starting to come forefront now. So a lot of institutions were working with are asking What's the next generation Roll off a CDO and why are they in the background and why are they not in the foreground? And this is when you become more often they were proactive with data and the digital officers are obviously focused on, you know, the transformation that has to happen. But the studios are their backbone in order to make the transformation. Really. And if the CDO started, think about their data as an asset did as a product did us a service. The judicial officers are right there because those are the real, you know, like the data data they're living so CDO can really become from my back office to really become a business line. We've >> seen taking the reins in machine learning in machine learning projects and cos you work with. Who >> was driving that? Yeah. Great question. So we are seeing, like, you know, different. I would put them in buckets, right? There is no one mortal fits all. We're seeing different generations within the company's. Some off. The ones were just testing out the market. There's two keeping it in their technology space in their back office. Take idea and, you know, in in forward I d let me call them where they are starting to experiment with this. But you see, the mature organizations on the other end of the spectrum, they are integrating action, learning and a I right into the business line because they want to see ex souls having the technology right by their side so they can lead leverage. Aye, aye. And machine learning spot right for the business right there. And that is where we're seeing know some of the new models. Come on. >> I think the big shift from a CDO perspective is using a i to prep data for a That's that's fundamentally where you know, where the data science was distributed. Some of that data science has to come back and free the integration for equality for data prepping because you've got all this data third party and other from customer streaming into the organization. And you know, the work that you're doing around, um, anomaly detection is it transcends developing the rules, doing the profiling, doing the rules. You know, the very manual, the very labor intensive process you've got to get away from that >> is used in order for this to be scale goes and a I to figure out which out goes to apply t >> clean to prepare the data toe, see what algorithms we can use. So it's basically what we're calling a eye for data rather than just data leading into a I. So it's I mean, you know, you developed a technology for one off our clients and pretty large financial service. They were getting closer, like 1,000,000,000 data points every day. And there was no way manually, you could go through the same quality controls and all of those processes. So we automated it through algorithms, and these algorithms are learning the behavior of data as they flow into the organization, and they're able to proactively tell their problems are starting very much. And this is the new face that we see in in the industry, you cannot scale the traditional data governance using manual processes, we have to go to the next generation where a i natural language processing and think about on structure data, right? I mean, that is, like 90% off. The organization is unstructured data, and we have not talked about data quality. We have not talked about data governance. For a lot of these sources of information, now is the time. Hey, I can do it. >> And I think that raised a great question. If you look at unstructured and a lot of the data sources, as you start to take more of an offensive stance will be unstructured. And the data quality, what it means to apply data quality isn't the the profiling and the rules generation the way you would with standard data. So the teams, the skills that CEOs have in their organizations, have to change. You have to start to, and, you know, it's a great example where, you know, you guys were ingesting documents and there was handwriting all over the documents, you know, and >> yeah, you know, you're a great example, Bob. Like you no way would ask the client, like, you know, is this document gonna scanned into the system so my algorithm can run and they're like, Yeah, everything is good. I mean, the deal is there, but when you then start scanning it, you realize there's handwriting and the information is in the handwriting. So all the algorithms breakdown now >> tribal knowledge striving Exactly. >> Exactly. So that's what we're seeing. You know, if I if we talk about the digital transformation in data in the city organization, it is this idea dart. Nothing is left unseen. Some algorithm or some technology, has seen everything that is coming into. The organization has has has a para 500. So you can tell you where the problems are. And this is what algorithms do. This scale beautifully. >> So the data quality approaches are evolving, sort of changing. So rather than heavy, heavy emphasis on masking or duplication and things like that, you would traditionally think of participating the difficult not that that goes away. But it's got to evolve to use machine >> intelligence. Exactly what kind of >> skill sets people need thio achieve that Is it Is it the same people or do we need to retrain them or bring in new skills. >> Yeah, great question. And I can talk from the inspector off. Where is disrupting every industry now that we know, right? But we knew when you look at what skills are >> required, all of the eye, including natural language processing, machine learning, still require human in the loop. And >> that is the training that goes in there. And who do you who are the >> people who have that knowledge? It is the business analyst. It's the data analyst who are the knowledge betters the C suite and the studios. They are able to make decisions. But the day today is still with the data analyst. >> Those s Emmys. Those sm >> means So we have to obscure them to really start >> interacting with these new technologies where they are the leaders, rather than just waiting for answers to come through. And >> when that happens now being as a data scientist, my job is easy because they're Siamese, are there? I deploy the technology. They're semi's trained algorithms on a regular basis. Then it is a fully fungible model which is evolving with the business. And no longer am I spending time re architect ing my rules. And like my, you know, what are the masking capabilities I need to have? It is evolving us. >> Does that change the >> number one problem that you hear from data scientists, which is the 80% of the time >> spent on wrangling cleaning data 10 15 20% run into sm. He's being concerned that they're gonna be replaced by the machine. Their training. >> I actually see them being really enabled now where they're spending 80% of the time doing boring job off, looking at data. Now they're spending 90% of their time looking at the elements future creative in which requires human intelligence to say, Hey, this is different because off X, >> y and Z so let's let's go out. It sounds like a lot of what machine learning is being used for now in your domain is clean things up its plumbing. It's basic foundation work. So go out. Three years after all that work has been done and the data is clean. Where are your clients talking about going next with machine learning? Bob, did you want? >> I mean, it's a whole. It varies by by industry, obviously, but, um but it covers the gamut from, you know, and it's generally tied to what's driving their strategies. So if you look at a financial service is organization as an example today, you're gonna have, you know, really a I driving a lot of the behind the scenes on the customer experience. It's, you know, today with your credit card company. It's behind the scenes doing fraud detection. You know, that's that's going to continue. So it's take the critical functions that were more data. It makes better models that, you know, that that's just going to explode. And I think they're really you can look across all the functions, from finance to to marketing to operations. I mean, it's it's gonna be pervasive across, you know all of that. >> So if I may, I don't top award. While Bob was saying, I think what's gonna what What our clients are asking is, how can I exhilarate the decision making? Because at the end of the day on Lee, all our leaders are focused on making decisions, and all of this data science is leading up to their decision, and today you see like you know what you brought up, like 80% of the time is wasted in cleaning the data. So only 20% time was spent in riel experimentation and analytics. So your decision making time was reduced to 20% off the effort that I put in the pipeline. What if now I can make it 80% of the time? They're I put in the pipeline, better decisions are gonna come on the train. So when I go into a meeting and I'm saying like, Hey, can you show me what happened in this particular region or in this particular part of the country? Previously, it would have been like, Oh, can you come back in two weeks? I will have the data ready, and I will tell you the answer. But in two weeks, the business has ran away and the CDO know or the C Street doesn't require the same answer. But where we're headed as as the data quality improves, you can get to really time questions and decisions. >> So decision, sport, business, intelligence. Well, we're getting better. Isn't interesting to me. Six months to build a cube, we'd still still not good enough. Moving too fast. As the saying goes, data is plentiful. Insights aren't Yes, you know, in your view, well, machine intelligence. Finally, close that gap. Get us closer to real time decision >> making. It will eventually. But there's there's so much that we need to. Our industry needs to understand first, and it really ingrained. And, you know, today there is still a fundamental trust issues with a I you know, it's we've done a lot of work >> watch Black box or a part of >> it. Part of it. I think you know, the research we've done. And some of this is nine countries, 2400 senior executives. And we asked some, ah, a lot of questions around their data and trusted analytics, and 92% of them came back with. They have some fundamental trust issues with their data and their analytics and and they feel like there's reputational risk material reputational risk. This isn't getting one little number wrong on one of the >> reports about some more of an >> issue, you know, we also do a CEO study, and we've done this many years in a row going back to 2017. We started asked them okay, making a lot of companies their data driven right. When it comes to >> what they say they're doing well, They say they're day driven. That's the >> point. At the end of the day, they making strategic decisions where you have an insight that's not intuitive. Do you trust your gut? Go with the analytics back then. You know, 67% said they go with their gut, So okay, this is 2017. This industry's moving quickly. There's tons and tons of investment. Look at it. 2018 go down. No, went up 78%. So it's not aware this issue there is something We're fundamentally wrong and you hit it on. It's a part of its black box, and part of it's the date equality and part of its bias. And there's there's all of these things flowing around it. And so when we dug into that, we said, Well, okay, if that exists, how are we going to help organizations get their arms around this issue and start digging into that that trust issue and really it's the front part is, is exactly what we're talking about in terms of data quality, both structured more traditional approaches and unstructured, using the handwriting example in those types of techniques. But then you get into the models themselves, and it's, you know, the critical thing she had to worry about is, you know, lineage. So from an integrity perspective, where's the data coming from? Whether the sources for the change controls on some of that, they need to look at explain ability, gain at the black box part where you can you tell me the inferences decisions are those documented. And this is important for this me, the human in the loop to get confidence in the algorithm as well as you know, that executive group. So they understand there's a structure set of processes around >> Moneyball. Problem is actually pretty confined. It's pretty straightforward. Dono 32 teams are throwing minor leagues, but the data models pretty consistent through the problem with organizations is I didn't know data model is consistent with the organization you mentioned, Risk Bob. The >> other problem is organizational inertia. If they don't trust it, what is it? What is a P and l manage to do when he or she wants to preserve? Yeah, you know, their exit position. They attacked the data. You know, I don't believe that well, which which is >> a fundamental point, which is culture. Yes. I mean, you can you can have all the data, science and all the governance that you want. But if you don't work culture in parallel with all this, it's it's not gonna stick. And and that's, I think the lot of the leading organisations, they're starting to really dig into this. We hear a lot of it literacy. We hear a lot about, you know, top down support. What does that really mean? It means, you know, senior executives are placing bats around and linking demonstrably linking the data and the role of data days an asset into their strategies and then messaging it out and being specific around the types of investments that are going to reinforce that business strategy. So that's absolutely critical. And then literacy absolutely fundamental is well, because it's not just the executives and the data scientists that have to get this. It's the guy in ops that you're trying to get you. They need to understand, you know, not only tools, but it's less about the tools. But it's the techniques, so it's not. The approach is being used, are more transparent and and that you know they're starting to also understand, you know, the issues of privacy and data usage rights. That's that's also something that we can't leave it the curb. With all this >> innovation, it's also believing that there's an imperative. I mean, there's a lot of for all the talk about digital transformation hear it everywhere. Everybody's trying to get digital, right? But there's still a lot of complacency in the organization in the lines of business in operation to save. We're actually doing really well. You know, we're in financial service is health care really hasn't been disrupted. This is Oh, it's coming, it's coming. But there's still a lot of I'll be retired by then or hanging. Actually, it's >> also it's also the fact that, you know, like in the previous generation, like, you know, if I had to go to a shopping, I would go into a shop and if I wanted by an insurance product, I would call my insurance agent. But today the New world, it's just a top off my screen. I have to go from Amazon, so some other some other app, and this is really this is what is happening to all of our kind. Previously that they start their customers, pocketed them in different experience. Buckets. It's not anymore that's real in front of them. So if you don't get into their digital transformation, a customer is not going to discount you by saying, Oh, you're not Amazon. So I'm not going to expect that you're still on my phone and you're only two types of here, so you have to become really digital >> little surprises that you said you see the next. The next stage is being decision support rather than customer experience, because we hear that for CEOs, customer experience is top of mind right now. >> No natural profile. There are two differences, right? One is external facing is absolutely the customer internal facing. It's absolutely the decision making, because that's how they're separating. The internal were, says the external, and you know most of the meetings that we goto Customer insight is the first place where analytics is starting where data is being cleaned up. Their questions are being asked about. Can I master my customer records? Can I do a good master off my vendor list? That is where they start. But all of that leads to good decision making to support the customers. So it's like that external towards internal view well, back >> to the offense versus defense and the shift. I mean, it absolutely is on the offense side. So it is with the customer, and that's a more directly to the business strategy. So it's get That's the area that's getting the money, the support and people feel like it's they're making an impact with it there. When it's it's down here in some admin area, it's below the water line, and, you know, even though it's important and it flows up here, it doesn't get the VIN visibility. So >> that's great conversation. You coming on? You got to leave it there. Thank you for watching right back with our next guest, Dave Lot. Paul Gillen from M I t CDO I Q Right back. You're watching the Cube

>> live from Las Vegas. It's the queue covering del Technologies. World twenty nineteen. Brought to you by Del Technologies and its ecosystem partners. >> Welcome back to Deal Technologies, World twenty nineteen. I'm stupid and my co host Dave Volante. Two sets, three days wall to wall coverage. Everything going on in Del Technologies really happen? A. Welcome back to the program. Same grow Cotton. Who's the senior vice president of product marketing at Delhi Emcee Sam so much that >> I am psyched to be here. I'm so excited. >> So you know you know, David, I will talk. You know, we come to these shows and back in the M C World days. It was like, Okay, let's walk through this massive portfolio and all the different areas. Last year we talked. There's a lot of simplification going on on DH Boy. This year it felt like, you know, massive infusion of cloud and talked to a lot of your team about how what's really happening now. It's not cloud walking. We're well past that. You know, Emcee and Dell both, you know, road through a lot of that today. But, you know, take us inside the keynote, putting these things together, and it's still quite a massive portfolio. >> It is, it is, and I get the honor of being the kind of the marketing front for the entire Delhi in C portfolio. So whether it's stored server networking, data protection and now hyper convert conversion now cloud our newest member of kind of the family, so to speak, Um, I get the opportunity kind of represent that which the earlier point creates a challenge as well, because it's such a broad portfolio of technology. So any time we get the opportunity to come. Teo Adult Technologies World of'em world rather a big event. We want to make sure we we shined the brightest light on the products that air >> both >> new and innovative, as walls continue to grow at a high rate. >> Alright, So Sam challenge. I wonder if I'm seeing a little bit of trend in there. So year ago, power Max was unveiled. We talk to the data protection team. It's power. Protect the the networking stuff got re branded with power and they've got the shirt with the lift switch power switch on there. So, you know, am I sensing a trend? Here is the When we simplify the portfolio. Power is the brand that lives up there. Are you the father of power? >> I am. To some degree. Yes, it was. It was kind of the genesis of an idea that we built on the original power edge brand which predate predated my arrival here. But we do. Look, we look, we look at the portfolio from a strategic lens and we're looking at the various different solutions we have across all the storage high end, mid range on structured as to the server product lines. Now, we powered up the data protection with power. Protect your point. Power switch is now on. So we turned. That went on, and we will continue to power up the rest of portfolio. So you're definitely on to something. There is a trend here, multiple points on that trend line. And I think you should be excited to know there's a lot more to come there too. >> So what? People talk about large portfolios. There was talk about integration and sort of threads across the architecture that maybe brings them together from a marketing standpoint and messaging standpoint. What are some of those threads that you're weaving through the portfolio, >> right? So one of the unique opportunities we have with such a broad portfolios, we want to make sure we have very strong, hard hitting product messaging. So of course, you've got the typical storage and data protection server messaging that talk about the he customer dynamics and trends that are going on at the individual product level. Now, what's what's newer this year and what you'LL start to see? More of us. We go for it is right now taking that product approach now, going vertical with that, talking about solutions and workloads and applications. So the big opportunity we have. And you saw that with the introduction of Del Technology Cloud as well as the Del Technologies Unified workspace, because we're now telling a broader solution story that includes, frankly, many products within delancy and many products across the broader del technology family that provide more of a business outcome solution, outcome discussion for our customers, complimenting the strong kind of individual piece part discussions which we have >> you and Sam, you know, we've looked at some of those solutions for a number of years, you know, VM wear and pivotal, and the storage products have been put together for a lot. Something I saw more than ever is you know, they're they're baked together. If you know VCF on top of it, the whole SPDC snack, you know, big day. One key note was a lot about the talk of, you know the better. Together as the pieces gives a little bit of insight, as you know how closely you know Del and the other logo's on the banner are working together. >> Yeah, if you think about over the last few years, Better together has been a big focus of ours is, especially as we've come together as one large company. But I would say we lived in the same neighborhood, you know. Now we live in the same house and and it's it's about how do we have the best integration between one product line or one room of the house with our neighboring room of the house for another product line? And you've seen that most recently with VX rail with the V C and technology and the delicacy of a structure. But now you're seeing it even broader than that. Del Technology Cloud is my favorite one to talk about, of course, and that is that bringing together the VM where Cloud Foundation suite of software This amazing set of software combined with this market leading segment leading delicacy infrastructure to provide that end and Turkey on premise Hybrid cloud which now could goto azure or Amazon >> Dave gives a whole another meaning to the noisy neighbor problem like >> All right, I'm gonna ask you So when you were >> living, it's a fun house. It's a very fun house. >> So when you were with Isil on, you had a relationship obviously with GM, where you got the S d. K. And you would do it then because you get acquired by CMC. VM wears sort of a sister company. Um law. Oftentimes the emcee would argue, Well, our integration is better than net APS or whoever else is. And, you know, maybe it was. Maybe it wasn't fine compete. But today there seems to be a conscious effort to really drive integration across the portfolio using VM. Where is the linchpin? I wonder if you could talk about that in terms of the strategy and what it means in terms of product marketing. >> Yeah, so it really depends on the case or work loader solution. Certainly in the cloud, I think, Dave, you're dead. On the VM are Virtual Cloud Foundation suite is the linchpin is the operating hub for our hybrid crowd saggy sitting on top of our infrastructure? So So that is absolutely the case. But if you look at other solutions there, maybe there's another member of this extended family that should be the point, or should be the lead of of kind of charge into a specific work. Hillary's case. We'LL evaluate those on a case by case basis. I think the important thing, though, is the strategy stops start from the top with Patton Jeff really working with both of'Em were and l N c teams. It is super clear the prioritization, the focus in the alignment to go build these combined solutions Together, we may not have had that alignment in the past, So if you look back historically, way probably didn't execute a CZ well or as fast as we wanted were now operating in absolute alignment and synchronization on the strategy, which makes it really easy for the teams to operate. Whether it's a marketing team, an engineering team, a services team, we're absolutely in locks >> up fascinated by this. Why? What's changed? What is it that Dell has brought to this culture that has enabled that catalyzed that? >> I think, you know, starting at the top with Michael, but certainly patent. Jeff spent the time, I think, Jeff, over a year and a half ago, they sat down and said, Here are key strategic tenants. Here's what we need to go do as better Together, we think we can move faster in the market. We aligned on those priorities, and we execute on those every single day. So I think that day one alignment has really helped to make the change >> very, very quick. Sounds >> so simple. But if if the assumptions that they make it the top don't pan out, then you have to pivot and you see it all the time in the tech business. All right, We're going to take that hill. Okay, Right. Way took that hill, but nobody's buying that hill. So now we got to go over here and we gotta Is Johnston shifting? Yeah. So is that the secret sauce? At least part of it is that they got it right early on. Fast course correction. >> Yeah, So I think the hero example that we've had the most run time with is the VX rail, which I definitely think we've hit a grand slam right with that one. Now we're trying to replicate that. Any more complex solution is something that's not just in an appliance. It's more broader. It's more strategic. You're now extending into, uh, partners like public cloud players, so it's much more. It's very, very important to have a plan have a strategy aligned to that execute. But by no means are we heads down and just going to take the hill if if the environment changes if the facts change. Jeff Pat the extended teams we constantly reevaluate and way were nimble and agile. We'LL shift if we have to. >> So, Sam, we've spent a lot of time digging in with the storage team here. I went through three Expo Hall, lots of gear you can touch, let two demos you can do. There's some people you know, went to the keynote, and they're like, Oh my gosh, this is not M c world. There's not that much storage. It kind of got glossed over when you talk about cloud and converged in all these things, they're talking about how you balance that internally and from out from a messaging standpoint, you know, Where is the message in the state of storage? You know, today in twenty nineteen? >> Yes. Oh, So yesterday we really focused on the Del technology solutions. Don't that cloud they'LL take unified workspace. Today's Kino we really pivoted back to the infrastructure conversation. This is where you saw the new enhancements with the unity x t. The ice salon continued to advance data protection with the new power protect announcements. So I would say day to probably felt more familiar for the traditional end SeaWorld teams. We had great demos showcasing The new capabilities were able tio have great customer examples how they're taking advantage of these capabilities. But with a portfolio so broad at Delta at the Del technologies level, never mind the deli in sea level, you have to pick and choose. And how you message to your customers, your partners to all of you. Of course. Well, so what? We're trying to kind of a line a solution story that's then complimented by great best of breed individual piece parts. And I think he saw that balance over day one and Day two today. How >> do >> you measure your success from A from a marketing standpoint? I mean, is it just revenue? I mean that, obviously one, but it's removed. But I mean, what other metrics do you use to sort of inform your strategy? >> Yes. Oh, again, I I had the pleasure of working both for Jeff Clark and Ellison do so. I actually have two bosses, which is a lot of fun, at times, literally. Seriously. Report dual report to both them. And what's great about that is there is no air gap between the marketing accountability, the marketing goals and objectives with the business within De Liam Si eso look, the ultimate factor that we look at in additional revenue, its market share. Are we competing in the markets that we select to compete in? And are we taking share? We've had a great last day, uh, great run over last year and a half on that front. So that goal is the same goal that we drive within marketing. Yes, there's things like share, voice and pipeline. You know, traditional marketing factors that we count within marketing to evaluate how things are working but were absolutely focused on the on ly goal. No legal that matters is hitting the plan hidden in the revenue growth and taking chair from our >> competitive. And so the cheese market share, I presume. Use I d see data as least in part. Maybe, maybe garden data. It's a combination of Yes. Okay, how's the market data? Because markets so huge we heard today with Pat Kelsey was talking today about two trillion dollar market, you know, And I say to myself, Well, how do you even measure? You know, the various segments in such a big market where there's been such consolidation, But what have you found in terms of the consistency and the accuracy, the data in terms of how it's translated to mean? Ultimately you can you can tell by your revenue growth, comparing it to others, revenue growth. So there's that measure, but is it pretty much stable and you're able tto? Is >> it reasonably predictable? You know, I won't get into the specifics, but we have a very detailed process on how we measure our success or not way Do use various resource is in terms of I. D. C and others to kind of measure in judge how the market's going. I would say it's an input. It's not the exact science that we would certainly certainly follow, but to your earlier discussion on Do things change? Obviously, market predictions, if I ever tell you three years from now with the market, is you know I would be a genius and Nostra Thomas and I would be predicting a lot of other things. It changes constantly. What we do know is the overall market is growing very quickly. It's in an unpredictable state of growth because of the amount of data that is growing. We think from a deli in C infrastructure standpoint, there is going to require a lot more infrastructure. So we feel very good about where the market is going in our role within this data era that we talked about today. But whether it's us or the market predictors, everybody is constantly adjusting because you just don't know >> what you have. Other sources you have obviously the channel you have. You you talk to customers. I mean, okay, Tom suite was selling us. That, I think is I. D. C. Was saying that it is going to grow it spendings and go to ex uh GDP, which I'm intrigued by on I believe it. I just Historically, it's such a big market. It's been aligned with GDP, but it does feel like it's it's accelerating faster. >> Look at the gross. I mean, look at that. The tech trends five g The emergence of the eye ot Internet of things at the edge Thie advancements within the modernizing of infrastructure. The move Teo hyper converge these new cloud solutions as we look to provide a non Prem cloud. You look at the public, Claude vendors are now have taken notice and said, Hey, you know what? It's not all one way or the other way. We've got to get into that game as well. So you're seeing a tremendous amount of growth, a tremendous amount of opportunity. At the end of the day, how are we helping our customers digitally transform is our goal in our mission, and I think we've got a great track record doing that in the >> world. Nothing in your size, a little bit of growth. There's a lot of >> cash, Sam, I don't want to give you the final word. You talk about the digital transformation. Give us a little bit of insight to the customers you're talking about. Where they are in their journeys has come the biggest challenges and opportunities that they're facing today. >> Look, we've been talking about digital digital transformation for a few years now. I would say we're still in the early innings. You certainly have a lot more customers that are taking advantage of digital transformation in typically lines of business, but not necessarily wholesale transformation. So I would say we're seeing a lot more customers seeing a lot more success in line of business conversion to digitally transform. But the next wave a transformation is hold hold, wholesale business transformation. You got a few highlights here and there. But for companies that are not born in this world that are more of a traditional business, it's the early early innings. So I think it's crazy, tremendous opportunity for everyone. Alright, >> well, Sam, first off, congratulations. We know it's not just the event, but all the different pieces that come through take more than a year for all these pieces together. So congratulations so >> much that they love the partnership. Looking forward to seeing you guys at the next big event. >> All right, for David, Dante, I'm Stew Minutemen. Be back with more coverage here from Del Technologies, World twenty nineteen in Las Vegas. Thank you for watching the cue.

>> live from Orlando, Florida It's the que covering accelerate nineteen. Brought to you by important. >> Welcome back to the Cube. We air live in Orlando, Florida, for accelerate border, not accelerate twenty nineteen. Lisa Martin with Peter Burroughs And we're pleased to welcome back to the Cube. Chief information Security officer See, So from Fortinet fell quite Phil. Thank you so much for joining Peter and me on the Cube today. >> Thanks for much. >> So lots of news, Lots of buzz. You can hear a lot of the folks behind us in the Expo hall here. We've had probably, I think five or six or seven guests today. So far you are on the front lines as to see so afforded it talking with si sos. I'd love to get your your view on what are some of the things they're top of mind for si sos today. The challenge is that they're facing and how are they looking to for doughnut to mitigate this challenge is >> the good news is that the solution sets not as complicated a cz youthink So all the sea says and senior people I talked to are very much focused on How can they reduce complexity, And how can they better leverage automation? I know there's some overlap between those two things, but they care quite a bit about that. Why? Because with less complexity, there's less mistakes with less complexity. There's less optics, right costs, costs for people and then with automation. It also helps with the op ex problem. But automation also allows humans to do things that humans you're better doing things. That and let's machines do things that their better doing that. So, complexity management, Lebanese automation are really top of mind. Of course, you know the next level down, you really need to do segmentation. Well, you need to have good visibility, need to inspection something that But I'd say those couple things are definitely top of mind, no matter who you talked to. >> But one of the things that's especially important about this issue complexity is that the threat surface goes to value, right so that, as you think about I ot as you think about Mohr devices, Mohr elements, et cetera, the threat surface is going to go up the on ly way that you're going to be able to bring that in in a managed way that delivers consistent value without dramatically exploding amount of risk is to reduce the complexity of the rest of the threat >> surface. Thie. If you're trying to place the face the problem of of, of speed and scale, you have to adopt the solutions of automation in integration you need a strategy on. Of course, hope is never a strategy, and so you need to leverage these technologies to do that. Instead, it's all about automation integration, >> right on this notion of the threats surface going to values, gotta have some mean si SOS sort of. Some of the ones that I'm talking to are using terminology like that. Maybe not that concept directly, but they want to make sure that whatever task that they're performing, whatever, uh, whatever risked their engendering or dealing with has some corresponds back to value. Are you seeing that as well? >> Yeah, and since we're talking about value, the end point is becoming a whole lot more interesting in terms of value. So traditionally we think of the endpoint is being a place where there's desktops and then laptops and tablets, and now smartphones, and they've always been part of our cyber domain. But there's this new thing that's happening, I think just left of end point. And it's where there's going to be the heavy instrumentation of physical processes and things. So it's starting with OT operational technology. It's going to be magnified by I ot and, of course, building automation. And so all of a sudden, the definition of value, I think, is going to be places that can collect data about physical processes and things, protect that data and then commoditize it. So value is moving further, further and out into the endpoint defined as thie, a collection of information about physical processes, something so I call this environment cyber physical or, more specifically, more catchy. SciFi right. It's where cyber data, physical data will be intermingled to provide value and efficiencies to customers and things like that. It's a really important area that's the new in point >> in physics. We talk about transducers, right? The transducer is something that takes one form of energy and turns into another form of energy so they could perform a different kind of work. We're talking about what we call information transducers the idea, take one form of information and turn it into another form of information so that it can perform work that's seminal to this notion that you're describing with the side fi. >> That's a >> great analogy. I haven't heard it described that way before. It's kind of like, you know, back in the day where where people use fire to heat and people used sales to move things right. And one day >> it was a more >> wins, right? Wait, move sales. Sorry. Wings. Yeah. Okay. And, uh, someone saw the story. As the story goes, someone saw a pot on the fire, a kettle full of water boil and the lid of the pop move. So they realized I can use heat to move it. So they started integrating different ways of doing things to achieve new effects. And I think that's what you're talking about. He used the word trans transducer, but I think it's the same thing. And how can you use things previously kept separate to do things that you previously couldn't D'Oh. >> So let's talk about this SciFi era C y dash p H y. For those watching at home, what are some of the security challenges that this brings, but also the opportunities to be uncovered by that boiling point analogy. >> Yeah, if you don't mind, I'LL start with the start with positives right where the was a potential benefits to society. So we are all of us and everyone behind us. We're creatures, the physical domain and the opportunities that there will be new data connected about this physical domain that can affect his very personally. So in cyberspace, its ultimate a virtual world. So there could be compromises in cyberspace. That effect is in cyber ways, but when you have compromises in the physical domain, it could be a lot more personal. So let's say that you have a medical device or you have a something else that instruments the temperature, heat, humidity, vector, you name it. Failures in those areas can have a really profound effect on a negative way in this physical oriented domain. So now the flip side of that is because it has a very, very positive effect, Right? Thes healthcare devices could bring new conveniences or perhaps even help address some very important things where they'd be physical or mental disabilities weaken instrument very heavily how we create food products. And so maybe this heavy instrumentation of how you create food can help address world hunger. I know I'm getting kind of heavy about this, but heavy instrumentation of this physical domain has a lot of promise. Now back to the other side. It also has a lot of responsibility involved, right, because, as I mentioned earlier, we're creatures of the physical domain. So if you get it wrong, you could mess up something really important to our health. Care for our transportation, Andi. We also have a very strong feeling towards privacy. At some point, collecting too much about us physically is just too much. So you need to make sure that that any sense data's you have privacy protections built it. So like anything with great opportunity. There's great challenges involved. But by giving their name and starting described, this challenge is we are. We're one step down the path, I think. >> But if we take that and then turn it into a set of cyber security challenges, no secure network challenges, that one of the other things you describe is we're constantly learning about what are the characteristics of a good, competent, reasonable interface between the physical and digital worlds. That knowledge then has to be put back into how we handle network security. >> That's right. I like your use of the word knowledge. And earlier today I gave a talk about something I'm calling a digital big bang. It's an analogy of that. We had a digital big bang fifty years ago where an explosion of data is among us and there's some challenges will get back that in a second. The analogy is thie cosmic big bang of fourteen billion years ago. And it wasn't until we started certain had a quest for knowledge about the fundamental elements of the cosmic Big Bang and the hard sciences behind it. Physics, chemistry, biology, things like that that we actually started obtaining an accumulating knowledge. So I think to your point, there's a lot of knowledge accumulation that we need to start a quest for in this cyber physical domain. And that's that's all about treating cybersecurity more like a science rather than an art. And I think this cyber SciFi domain is a great place to start practicing that accumulating knowledge in a very, very scientific way, build on the build on the successes of our our forefathers. I could say >> Sorry if I can build on this for one second. Sorry, Lisa, that the entropy gets everything in the end. But isn't it interesting that the process of creating Mohr information creating more knowledge and then securing it is our main fight against entropy? Right. That's how we create increase optimization of our resources. How we get Maura out of less on DH. That seems to me to be an especially important thing here. A CZ we think about it is how we utilize that knowledge, share it and in so doing security so that we're sharing inappropriately. >> There's a there's a great saying. I'm sure you're familiar with each of you. It's called. I use it often. Data is the oil the twenty first century, right? So the last century, those who could find oil explored it put it good use and protected dominated that century. Let's fast forward to the twenty first century. I think the same words apply data right. Those who can find it generate wisdom from it, insight from it and protect it will dominate in a good way the twenty first century. So, on the way you were going to do that. This is the collective we is bias. You said Collect, Ate it. Make it better. Send it back out, bring it back in. Make it better. Send it back out. It's a somewhat circular, but I think it's a very, very healthy example of, ah, circular augmentation. >> So don't think I want to touch on a little bit with you. Feel before we let you go is we talked about knowledge a minute ago and sharing that knowledge forty nights Very dedicated to education. Educating your customers, educating your partners When you're talking with si SOS and we know that there's an ostensible skills got with cyber security. What are some of the solutions that you talked to those customers that like Hey, this is how fourteen that nurse ecosystem partners can help you here. Address this so you can leverage the power of that data to, As you said, you know, for the twenty first century, for example, data becomes the new oil. What's that education conversation like there's >> a There's a long game in a short game, you know, the short game is about leveraging like we talked about a few minutes ago. Speed, speed, automation, integration, too. Compliment the shortage of human beings right rely machines, moron for what machines we're good at on DH. Take the humans, the humans, the steel personnel and have them do the higher order thinking. So the near term game. It's foreign. It's really well. Pasha provide our customers is speed, automation and integration. So that's the short game. Long game is about creating, Ah, larger workforce or larger population of folks who could all be construed contribute to this great new world we've been talking about. And that's training. And that's education. And I think, you know important. It's also, you know, working the long game as well, with some near term training at multiple levels for folks in in the networking world, but were also part of something called the World Economic Form West's Center for Cyber Security. We're founding member, and there were trying to create a long game where we can help educate a whole lot of people on cybersecurity and create the future. Workforce is in the long game. So short term long game, both her important >> except well, Phil, thank you so much for joining Peter and me on the cute this afternoon. We appreciate your time. >> Thanks again. It was nice. Nice being back and >> excellent. Our pleasure for Peter. Boris. I'm Lisa Martin. You're watching the Cube

>> From our studios in the heart of Silicon Valley. HOLLOWAY ALTO, California It is a cube conversation. >> Welcome to the special. Keep conversation here in Palo Alto, California. I'm John for a co host of the Cube. Were Arsenal was a C I S O. C. So for Microsoft also corporate vice President, Chief information security. Thanks for joining me today. >> Thank you. >> Appreciate it. Thanks. So you have a really big job. You're a warrior in the industry, security is the hardest job on the planet. >> And hang in sight >> of every skirt. Officer is so hard. Tell us about the role of Microsoft. You have overlooked the entire thing. You report to the board, give us an overview of what >> happens. Yeah. I >> mean, it's you know, obviously we're pretty busy. Ah, in this world we have today with a lot of adversaries going on, an operational issues happening. And so I have responsibility. Accountability for obviously protecting Microsoft assets are customer assets. And then ah, And for me, with the trend also responsibility for business continuity Disaster recovery company >> on the sea. So job has been evolving. We're talking before the camera came on that it's coming to CEO CF roll years ago involved to a business leader. Where is the sea? So roll now in your industry is our is a formal title is it establishes their clear lines of reporting. How's it evolved? What's the current state of the market in terms of the sea? So it's roll? >> Yeah, the role is involved. A lot. Like you said, I think like the CIA or twenty years ago, you know, start from the back room of the front room and I think the, you know, one of things I look at in the role is it's really made it before things. There's technical architecture, there's business enablement. There's operational expert excellence. And then there's risk management and the older ah, what does find the right word? But the early see so model was really about the technical architecture. Today. It's really a blend of those four things. How do you enable your business to move forward? How do you take calculated risks or manage risks? And then how do you do it really effectively and efficiently, which is really a new suit and you look at them. You'LL see people evolving to those four functions. >> And who's your boss? Would you report to >> I report to a gentleman by the name of a curtain. Little Benny on DH. He is the chief digital officer, which would be a combination of Seo did officer and transformation as well as all of Microsoft corporate strategy >> and this broad board visibility, actually in security. >> Yeah, you >> guys, how is Microsoft evolved? You've been with the company for a long time >> in the >> old days ahead perimeters, and we talk about on the Cube all the time. When a criminalist environment. Now there's no perimeter. Yeah, the world's changed. How is Microsoft evolved? Its its view on security Has it evolved from central groups to decentralize? How is it how how was it managed? What's the what's the current state of the art for security organization? >> Well, I think that, you know, you raise a good point, though things have changed. And so in this idea, where there is this, you know, perimeter and you demanded everything through the network that was great. But in a client to cloak cloud world, we have today with mobile devices and proliferation or cloud services, and I ot the model just doesn't work anymore. So we sort of simplified it down into Well, we should go with this, you know, people calls your trust, I refer to It is just don't talk to strangers. But the idea being is this really so simplified, which is you've got to have a good identity, strong identity to participate. You have to have managed in healthy device to participate, to talk to, ah, Microsoft Asset. And then you have to have data in telemetry that surrounds that all the time. And so you basically have a trust, trust and then verify model between those three things. And that's really the fundamental. It's really that simple. >> David Lava as Pascal senior with twenty twelve when he was M. C before he was the C E O. V M. Where he said, You know his security do over and he was like, Yes, it's going to be a do over its opportunity. What's your thoughts on that perspective? Has there been a do over? Is it to do over our people looking at security and a whole new way? What's your thoughts? >> Yeah, I mean, I've been around security for a long time, and it's there's obviously changes in Massa nations that happened obviously, at Microsoft. At one point we had a security division. I was the CTO in that division, and we really thought the better way to do it was make security baked in all the products that we do. Everything has security baked in. And so we step back and really change the way we thought about it. To make it easier for developers for end users for admin, that is just a holistic part of the experience. So again, the technology really should disappear. If you really want to be affected, I think >> don't make it a happy thought. Make it baked in from Day one on new product development and new opportunity. >> Yeah, basically, shift the whole thing left. Put it right in from the beginning. And so then, therefore, it's a better experience for everyone using it. >> So one of things we've observed over the past ten years of doing the Cube when do first rolled up with scene, you know, big data role of date has been critical, and I think one of the things that's interesting is, as you get data into the system, you can use day that contextually and look at the contextual behavioral data. It's really is create some visibility into things you, Meyer may not have seen before. Your thoughts and reaction to the concept of leveraging data because you guys get a lot of data. How do you leverage the data? What's the view of data? New data will make things different. Different perspectives creates more visibility. Is that the right view? What's your thoughts on the role of Data World Data plays? >> Well, they're gonna say, You know, we had this idea. There's identity, there's device. And then there's the data telemetry. That platform becomes everything we do, what there's just security and are anomalous behavior like you were talking about. It is how do we improve the user experience all the way through? And so we use it to the service health indicator as well. I think the one thing we've learned, though, is I was building where the biggest data repositories your head for some time. Like we look at about a six point five trillion different security events a day in any given day, and so sort of. How do you filter through that? Manage? That's pretty amazing, says six point five trillion >> per day >> events per day as >> coming into Microsoft's >> that we run through the >> ecosystem your systems. Your computers? >> Yeah. About thirty five hundred people. Reason over that. So you can Certainly the math. You need us. Um, pretty good. Pretty good technology to make it work effectively for you and efficiently >> at RC A Heard a quote on the floor and on the q kind of echoing the same sentiment is you can't hire your way to success in this market is just not enough people qualified and jobs available to handle the volume and the velocity of the data coming in. Automation plays a critical role. Your reaction to that comment thoughts on? >> Well, I think I think the cure there, John, those when you talk about the volume of the data because there's what we used to call speeds and feeds, right? How big is it? And I used to get great network data so I can share a little because we've talked, like from the nineties or whatever period that were there. Like the network was everything, but it turns out much like a diverse workforce creates the best products. It turns out diverse data is more important than speeds and feeds. So, for example, authentication data map to, you know, email data map to end point data map. TEO SERVICE DATA Soon you're hosting, you know, the number of customers. We are like financial sector data vs Healthcare Data. And so it's the ability Teo actually do correlation across that diverse set of data that really differentiates it. So X is an example. We update one point two billion devices every single month. We do six hundred thirty billion authentications every single month. And so the ability to start correlating those things and movement give us a set of insights to protect people like we never had before. >> That's interesting telemetry you're getting in the marketplace. Plus, you have the systems to bring it in >> a pressure pressure coming just realized. And this all with this consent we don't do without consent, we would never do without consent. >> Of course, you guys have the terms of service. You guys do a good job on that, But I think the point that I'm seeing there is that you guys are Microsoft. Microsoft got a lot of access. Get a lot of stuff out there. How does an enterprise move to that divers model because they will have email, obviously. But they have devices. So you guys are kind of operating? I would say tear one of the level of that environment cause you're Microsoft. I'm sure the big scale players to that. I'm just an enterprising I'm a bank or I'm an insurance company or I'm in oil and gas, Whatever the vertical. Maybe. What do I do if I'm the sea? So they're So what does that mean, Diversity? How should they? >> Well, I think they have a diverse set of data as well. Also, if they participate, you know, even in our platform today, we you know, we have this thing called the security graph, which is an FBI people can tap into and tap into the same graph that I use and so they can use that same graph particular for them. They can use our security experts to help them with that if they don't have the all the resource and staff to go do that. So we provide both both models for that to happen, and I think that's why a unique perspective I should think should remind myself of which is we should have these three things. We have a really good security operations group we have. I think that makes us pretty unique that people can leverage. We build this stuff into the product, which I think is good. But then the partnership, the other partners who play in the graph, it's not just us. So there's lots of people who play on that as well. >> So like to ask you two lines of questions. Wanting on the internal complex is that organizations will have on the external complexity and realities of threats and coming in. How do they? How do you balance that out? What's your vision on that? Because, you know, actually, there's technology, his culture and people, you know in those gaps and capabilities on on all three. Yeah, internally just getting the culture right and then dealing with the external. How does a C so about his company's balance? Those realities? >> Well, I think you raised a really good point, which is how do you move the culture for? That's a big conversation We always have. And that was sort of, you know, it's interesting because the the one side we have thirty five hundred people who have security title in their job, But there's over one hundred thousand people who every day part of their job is doing security, making sure they'LL understand that and know that is a key part we should reinforce everyday on DSO. But I think balancing it is, is for me. It's actually simplifying just a set of priorities because there's no shortage of, you know, vendors who play in the space. There's no shortage of things you can read about. And so for us it was just simplifying it down and getting it. That simplifies simplified view of these are the three things we're going to go do we build onerous platform to prioritize relative to threat, and then and then we ensure we're building quality products. Those five things make it happen. >> I'd like to get your thoughts on common You have again Before I came on camera around how you guys view simplification terminal. You know, you guys have a lot of countries, the board level, and then also you made a common around trust of security and you an analogy around putting that drops in a bucket. So first talk about the simplification, how you guys simplifying it and why? Why is that important? >> You think we supply two things one was just supplying the message to people understood the identity of the device and making sure everything is emitting the right telemetry. The second part that was like for us but a Z to be illustrative security passwords like we started with this technology thing and we're going to do to FAA. We had cards and we had readers and oh, my God, we go talk to a user. We say we're going to put two FAA everywhere and you could just see recoil and please, >> no. And then >> just a simple change of being vision letters. And how about this? We're just going to get rid of passwords then People loved like they're super excited about it. And so, you know, we moved to this idea of, you know, we always said this know something, know something new, how something have something like a card And they said, What about just be something and be done with it? And so, you know, we built a lot of the capability natively into the product into windows, obviously, but I supported energies environment. So I you know, I support a lot of Mac clinics and IOS and Android as well So you've read it. Both models you could use by or you could use your device. >> That's that. That's that seems to be a trend. Actually, See that with phones as well as this. Who you are is the password and why is the support? Because Is it because of these abuses? Just easy to program? What's the thought process? >> I think there's two things that make it super helpful for us. One is when you do the biometric model. Well, first of all, to your point, the the user experience is so much better. Like we walk up to a device and it just comes on. So there's no typing this in No miss typing my password. And, you know, we talked earlier, and that was the most popular passwords in Seattle with Seahawks two thousand seventeen. You can guess why, but it would meet the complexity requirements. And so the idea is, just eliminate all that altogether. You walk up machine, recognize you, and you're often running s o. The user experience is great, but plus it's Actually the entropy is harder in the biometric, which makes it harder for people to break it, but also more importantly, it's bound locally to the device. You can't run it from somewhere else. And that's the big thing that I think people misunderstanding that scenario, which is you have to be local to that. To me, that's a >> great example of rethinking the security paradigm. Exactly. Let's talk about trust and security. You you have an opinion on this. I want to get your thoughts, the difference between trust and security so they go hand in hand at the same time. They could be confused. Your thoughts on this >> well being. You can have great trust. You can, so you can have great security. But you generally and you would hope that would equate like a direct correlation to trust. But it's not. You need to you build trust. I think our CEO said it best a long time ago. You put one bucket of water, one bucket. Sorry, one truffle water in the bucket every time. And that's how you build trust. Over time, my teenager will tell you that, and then you kick it over and you put it on the floor. So you have to. It's always this ratcheting up bar that builds trust. >> They doing great you got a bucket of water, you got a lot of trust, that one breach. It's over right, >> and you've got to go rebuild it and you've got to start all over again. And so key, obviously, is not to have that happen. But then, that's why we make sure you have operational rigor and >> great example that just totally is looking Facebook. Great. They have massive great security. What really went down this past week, but still the trust factor on just some of the other or societal questions? >> Yeah, >> and that something Do it. >> Security. Yeah, I think that's a large part of making sure you know you're being true. That's what I said before about, you know, we make sure we have consent. We're transparent about how we do the things we do, and that's probably the best ways to build trust. >> Okay, so you guys have been successful in Microsoft, just kind of tight the company for second to your role. It's pretty well documented that the stock prices at an all time high. So if Donatella Cube alumni, by the way, has been on the cue before he he took over and clear he didn't pivot. He just said we'd go in the cloud. And so the great moves, he don't eat a lot of great stuff. Open source from open compute to over the source. And this ship has turned and everything's going great. But that cheering the cloud has been great for the company. So I gotta ask you, as you guys move to the cloud, the impact to your businesses multi fold one products, ecosystem suppliers. All these things are changing. How has security role in the sea? So position been impact that what have you guys done? How does that impact security in general? Thoughts? >> Yeah, I think we obviously were like any other enterprise we had thousands of online are thousands of line of business applications, and we did a transformation, and we took a method logical approach with risk management. And we said, Okay, well, this thirty percent we should just get rid of and decommission these. We should, you know, optimize and just lifting shifting application. That cloud was okay, but it turns out there's massive benefit there, like for elasticity. Think of things that quarterly reporting or and you'll surveys or things like that where you could just dynamically grow and shrink your platform, which was awesome linear scale that we never had Cause those events I talk about would require re architectures. Separate function now becomes linear. And so I think there is a lot of things from a security perspective I could do in a much more efficient must wear a fish. In fact, they're then I had to have done it before, but also much more effective. I just have compute capability. Didn't have I have signal I didn't have. And so we had to wrap her head around that right and and figure out how to really leverage that. And to be honest, get the point. We're exploited because you were the MySpace. I have disaster and continent and business. This is processed stuff. And so, you know, everyone build dark fiber, big data centers, storage, active, active. And now when you use a platform is a service like on that kind of azure. You could just click a Bach and say, I want this thing to replicate. It also feeds your >> most diverse data and getting the data into the system that you throw a bunch of computer at that scale. So What diverse data? How does that impact the good guys and the bad guys? That doesn't tip the scales? Because if you have divers date and you have his ability, it's a race for who has the most data because more data diversity increases the aperture and our visibility into events. >> Yeah, I you >> know, I should be careful. I feel like I always This's a job. You always feel like you're treading water and trying to trying to stay ahead. But I think that, um, I think for the first time in my tenure do this. I feel there's an asymmetry that benefits. They're good guys in this case because of the fact that your ability to reason over large sets of data like that and is computed data intensive and it will be much harder for them like they could generally use encryption were effectively than some organization because the one the many relationship that happens in that scenario. But in the data center you can't. So at least for now, I feel like there's a tip This. The scales have tipped a bit for the >> guy that you're right on that one. I think it's good observation I think that industry inside look at the activity around, from new fund adventures to overall activity on the analytics side. Clearly, the data edge is going to be an advantage. I think that's a great point. Okay, that's how about the explosion of devices we're seeing now. An explosion of pipe enabled devices, Internet of things to the edge. Operational technologies are out there that in factory floors, everything being I P enables, kind of reminds me of the old days. Were Internet population you'd never uses on the Internet is growing, and >> that costs a lot >> of change in value, creation and opportunities devices. Air coming on both physical and software enabled at a massive rate is causing a lot of change in the industry. Certainly from a security posture standpoint, you have more surface area, but they're still in opportunity to either help on the do over, but also create value your thoughts on this exploding device a landscape, >> I think your Boston background. So Metcalfe's law was the value the net because the number of the nodes on the network squared right, and so it was a tense to still be true, and it continues to grow. I think there's a huge value and the device is there. I mean, if you look at the things we could do today, whether it's this watch or you know your smartphone or your smart home or whatever it is, it's just it's pretty unprecedented the capabilities and not just in those, but even in emerging markets where you see the things people are doing with, you know, with phones and Lauren phones that you just didn't have access to from information, you know, democratization of information and analysis. I think it's fantastic. I do think, though, on the devices there's a set of devices that don't have the same capabilities as some of the more markets, so they don't have encryption capability. They don't have some of those things. And, you know, one of Microsoft's responses to that was everything. Has an M see you in it, right? And so we, you know, without your spirit, we created our own emcee. That did give you the ability to update it, to secure, to run it and manage it. And I think that's one of the things we're doing to try to help, which is to start making these I, O. T or Smart devices, but at a very low cost point that still gives you the ability because the farm would not be healed Update, which we learn an O. T. Is that over time new techniques happen And you I can't update the system >> from That's getting down to the product level with security and also having the data great threats. So final final talk Tracking one today with you on this, your warrior in the industry, I said earlier. See, so is a hard job you're constantly dealing with compliance to, you know, current attacks, new vector, new strains of malware. And it's all over the map. You got it. You got got the inbound coming in and you got to deal with all that the blocking and tackling of the organization. >> What do you What do >> you finding as best practice? What's the what if some of the things on the cso's checklist that you're constantly worried about and or investing in what some of >> the yeah, >> the day to day take us through the day to day life >> of visited a lot? Yeah, it >> starts with not a Leslie. That's the first thing you have to get used to, but I think the you know again, like I said, there's risk Manager. Just prioritize your center. This is different for every company like for us. You know, hackers don't break and they just log in. And so identity still is one of the top things. People have to go work on him. You know, get rid of passwords is good for the user, but good for the system. We see a lot in supply chain going on right now. Obviously, you mentioned in the Cambridge Analytical Analytics where we had that issue. It's just down the supply chain. And when you look at not just third party but forthe party fifth party supply and just the time it takes to respond is longer. So that's something that we need to continue to work on. And then I think you know that those are some of the other big thing that was again about this. How do you become effective and efficient and how you managed that supply chain like, You know, I've been on a mission for three years to reduce my number of suppliers by about fifty percent, and there's still lots of work to do there, but it's just getting better leverage from the supplier I have, as well as taking on new capability or things that we maybe providing natively. But at the end of the day, if you have one system that could do what four systems going Teo going back to the war for talent, having people, no forces and versus one system, it's just way better for official use of talent. And and obviously, simplicity is the is the friend of security. Where is entropy is not, >> and also you mentioned quality data diversity it is you're into. But also there's also quality date of you have quality and diverse data. You could have a nice, nice mechanism to get machine learning going well, but that's kind of complex, because in the thie modes of security breaches, you got pre breached in breech post breach. All have different data characteristics all flowing together, so you can't just throw that answer across as a prism across the problem sets correct. This is super important, kind of fundamentally, >> yeah, but I think I >> would I would. The way I would characterize those is it's honestly, well, better lessons. I think I learned was living how to understand. Talk with CFO, and I really think we're just two things. There's technical debt that we're all working on. Everybody has. And then there's future proofing the company. And so we have a set of efforts that go onto like Red Team. Another actually think like bad people break them before they break you, you know, break it yourself and then go work on it. And so we're always balancing how much we're spending on the technical, that cleanup, you know, modernizing systems and things that are more capable. And then also the future proofing. If you're seeing things coming around the corner like cryptography and and other other element >> by chain blockchain, my supply chain is another good, great mechanism. So you constantly testing and R and D also practical mechanisms. >> And there in the red team's, which are the teams that attacking pen everything, which is again, break yourself first on this super super helpful for us >> well bred. You've seen a lot of ways of innovation have been involved in multiple ways computer industry client server all through the through the days, so feel. No, I feel good about this you know, because it reminds me and put me for broken the business together. But this is the interesting point I want to get to is there's a lot of younger Si SOS coming in, and a lot of young talent is being attractive. Security has kind of a game revived to it. You know, most people, my friends, at a security expert, they're all gamers. They love game, and now the thrill of it. It's exciting, but it's also challenging. Young people coming might not have experience. You have lessons you've learned. Share some thoughts over the years that scar either scar tissue or best practices share some advice. Some of the younger folks coming in breaking into the business of, you know, current situation. What you learned over the years it's Apple Apple. But now the industry. >> Yeah, sadly, I'd probably say it's no different than a lot of the general advice I would have in the space, which is there's you value experience. But it turns out I value enthusiasm and passion more here so you can teach about anybody whose passion enthusiastic and smart anything they want. So we get great data people and make them great security people, and we have people of a passion like you know, this person. It's his mission is to limit all passwords everywhere and like that passion. Take your passion and driver wherever you need to go do. And I >> think the nice >> thing about security is it is something that is technically complex. Human sociology complex, right? Like you said, changing culture. And it affects everything we do, whether it's enterprise, small, medium business, large international, it's actually a pretty It's a fasten, if you like hard problem. If you're a puzzle person, it's a great It's a great profession >> to me. I like how you said Puzzle. That's I think that's exactly it. They also bring up a good point. I want to get your thoughts on quickly. Is the talent gap is is really not about getting just computer science majors? It's bigger than that. In fact, I've heard many experts say, and you don't have to be a computer scientist. You could be a lot of cross disciplines. So is there a formula or industry or profession, a college degree? Or is it doesn't matter. It's just smart person >> again. It depends if your job's a hundred percent. Security is one thing, but like what we're trying to do is make not we don't have security for developers you want have developed to understand oppa security and what they build is an example on DSO. Same with administrators and other components. I do think again I would say the passion thing is a key piece for us, but But there's all aspects of the profession, like the risk managers air, you know, on the actuarial side. Then there's math people I had one of my favorite people was working on his phD and maladaptive behavior, and he was super valuable for helping us understand what actually makes things stick when you're trying to train their educate people. And what doesn't make that stick anthropologist or super helpful in this field like anthropologist, Really? Yeah, anthropologist are great in this field. So yeah, >> and sociology, too, you mentioned. That would think that's a big fact because you've got human aspect interests, human piece of it. You have society impact, so that's really not really one thing. It's really cross section, depending upon where you want to sit in the spectrum of opportunity, >> knowing it gives us a chance to really hire like we hire a big thing for us has been hard earlier in career and building time because it's just not all available. But then also you, well, you know, hire from military from law enforcement from people returning back. It's been actually, it's been a really fascinating thing from a management perspective that I didn't expect when I did. The role on has been fantastic. >> The mission. Personal question. Final question. What's getting you excited these days? I mean, honestly, you had a very challenging job and you have got attend all the big board meetings, but the risk management compliance. There's a lot of stuff going on, but it's a lot >> of >> technology fund in here to a lot of hard problems to solve. What's getting you excited? What what trends or things in the industry gets you excited? >> Well, I'm hopeful we're making progress on the bad guys, which I think is exciting. But honestly, this idea the you know, a long history of studying safety when I did this and I would love to see security become the air bags of the technology industry, right? It's just always there on new president. But you don't even know it's there until you need it. And I think that getting to that vision would be awesome. >> And then really kind of helping move the trust equation to a whole other level reputation. New data sets so data, bits of data business. >> It's total data business >> breath. Thanks for coming on the Q. Appreciate your insights, but also no see. So the chief information security officer at Microsoft, also corporate vice president here inside the Cuban Palo Alto. This is cute conversations. I'm John Career. Thanks for watching. >> Thank you.

from Orlando Florida it's the cube covering si P sapphire now 2018 brought to you by net app welcome to the cube I'm Lisa Martin with Keith Townsend and we are in Orlando Florida that sa piece a fire now 2018 or in the net out booth really cool sa piece a fire is an enormous event this is like the 25th year they've been doing it and it's been really interesting to learn Keith about sa P and how they have really transformed and one of the things that's critical is their partner ecosystem so we're excited to welcome back to the cube a cube alumni Paul Young who is the director of sa P go to market from Google platform Paul it's nice to see you thanks so what is the current news with Google and sa P so you know I think we're making a major push into this three Marquette I think the the yesterday's announcements are we all still have a four tire buy on a server online but we also brought up capacity all the way up to 20 terabytes so we really can handle pretty much all the customer base at this point so on the one end that's good there is however a lot of other stuff we're doing in the AI space in the joint engineering space with SCP and and a lot of work we're doing in the make it a lot easier for SUV customers to adopt the cloud right and and beyond just what's happening a lot in the market right now which is you know 80 percent of the customers who mu and s pieces in the cloud just do straight lift and shift so there's no for momentum with a it's just ticking the box you're in the cloud we're doing a ton of work in engineering on our own and with SCP right now to make that a much more valuable journey for the customers so yeah I don't wake up in the morning at Google and think what am I going to do today it's you know it's a there's a lot of stuff going on so Paul let's not be shy that we've had you on the cube before and your ear s AP alone and as you look out at the hyper scalars the big cloud providers s ap more or less has a reference architecture for how to do cloud how to do s AP and a hyper scale of cloud but it's not just about that base capability when I when I talk to my phone I love asking Google questions when I look at you know capabilities like AI and tensor flow and machine learning that gets me excited just in general what as you looked out at the Haifa scalers what excited you about Google is specific as you we were s ap work to fall 3 so what's so exciting about Google I did I joke internally I was I was a customer of recipes for seven years I did 20 years of SVP and and yeah and and then woke up one morning and decided to go to Google yeah I do I get this question a lot on the yeah my conversation always is it wasn't based on the cafeteria food there are other things to join me across it seriously cuz in my last roll at scpi I was working with all three the hyper scalars and one of the questions I always got from SCP people is well they're all just the same right or and when you actually work with them you discover the are different and that's no disrespect to anyone but they approach the world differently they all have different business models and and the Google thing that really put me is that the the kind of engineering and the future focus was just tremendous right this other girl could do was was immense and so I said I'll jump forward to the future and then will come back but just if you look at the investment school was making in AI and machine learning all the stuff we were order a Google i/o with the the you know custom-built testable computers that can just do an amazing performance greatness or but it's got to be applied right so so things that partially built with Deloitte it's a deletion of the demonstration for it but just to give an example of where we think the future is we build a model in Nai where we have we basically two invoices and we taught the AI system to do data entry and SCP so that's not an interface we didn't say hey here's an invoice and here's all the fields and we map them all across and here's ETL and here's other things we do right here's our interface mapping we literally said imagine you're an AP processor how do you enter an invoice and you give it detail universities and it spends a lot of time doing really stupid things trying to put addresses in the number field of someone else and then suddenly it works so how to enter an invoice and at that point it knows how to enter an invoice and then what you do is you give it more and more invoices or more and more different structures and it learns how to what an invoice is and it learns how to process that and then suddenly it can do complete data entry right so we build as a model this is sort of thing Google does just to test the limits Deloitte came along and said well that's really cool could we actually take it and run it as a product and so the light now has that in there there are engineering further out where literally you can give it any invoice it will it's not OCR it will look at the invoice and it will work out that is an invoice where all the bits you need are from it it will then work out how you would do data entry on that into an SUV system and it will enter the invoice that's a future world where I know SUVs already launched the I our own doing three-way match interesting we're talking about future won't where your your entire accounts payable Department is a Gmail inbox where they mail you invoices that you've never seen before but we're able to understand what a vendor is grantee as a vendor guarantee is not fraud checked and do the deed to entry completely automatically that is the massive new world right and that's just a tiny little bit of what we can do at Google we have it just pretty also we haven't demo running on the booth where we have tensorflow looking at pure experience pharmaceuticals right right we have we have a demo run on the booth which is a graphic of someone we're actually running at customers where we have a camera reading pharmaceutical boxes as they go past or their pinky perfect curlers in this case but it doesn't just look at the box and say I count one box it reads the text on the box but it reads the text in the box was in noise from STP was supposed to be manufactured and it comes back and says well am I putting double-strength pills and single side boxes is this most legal have I mean sent the correct box is it you know is the packaging correct it also knows what a good box looks like and it learns what a damaged box looks like a nice packaging looks like an it knows how to reject them and again that level of technology where we can monitor all of your production lines and give you guarantee quality and pharmaceuticals anywhere else tell me six months ago anyone even imagined that was possible we're doing that right now all right that that ability to work with SCP because it's all integrated with SCP we're doing Depot of efficient that ability to deliver that sort of capability at the speed we deliver that is world-changing right well you know one of the things that I just kept imagining as you gwangsu the description of invoicing thankee was on a run of the day I'm a small business owner and these things are troublesome like you get in an invoice and I'm thinking you know I got a deal my my wife does the Council of payable accounts receivable I'm like there has to be a way to automate get but then I thought about just those challenges like you get one person says an invoice that the invoices at the bottom right hand corner the the invoice numbers on the bottom right hand corner the the amount due etcetera etc just really silly questions that AI should be AI machine learning should be able to deal with build mederma yesterday on stage says that AI should all been human capability and that's a great example of how a I augments you might take a bit and it doesn't in the AP example it doesn't do a hundred percent correct all the time right it knows what it's wrong in the example of Joey runs your seat comes up and says the dates wrong here I need to fix it so it's taken the it's taken the menial work out of the process and it's lighten people really add value in it but it's also a great example of the cloud at work and what it's supposed to do right again if all you do is take official SCP and drop it in the cloud you're just running in a different place if you get to a world where with Google we we don't expose your data to everybody else but we understand what the world's invoices look like and we have that knowledge and we make the entire world more efficient by having the model know how to work that's a radically better place right and that's that's that's there's just never been that value prop before and that's it's a great big exciting thing to wake up in the morning to think that's what we do right so Lisa in the industry we have this term that data has credit I think it's fairly safe at the this week we can say that processing technology compute has gravity it's we had another guest on it says that they use a process and a technology in solution and one customer works out fine and another customer not the same results it's this complexity is this kind of dish 'part of technology that is just not easy to apply across across companies so the other part really quickly that I want to talk about is you know this isn't just about AI right it's not just about the future I mean one of the key in me I said I'm a long-term HCV customer I work a lot of customers everybody wants to get to the cool bit you know and though I always used to joke internally everybody wants to eat candy they're ready vegetables first right and so we better get you across or you can candida vegetables whichever way you've got to eat both there's some point right so um so look just getting customers into the club becomes one of the challenges it's one of the other areas where we're really applying engineering so I'm three weeks ago we bought della Strada as an example Villa Stratos is an amazing company what well so it does basically it's a plug into VMware you drop it into VMware and it watches your SUV systems running it profiles them and it works out what size capacity you're going to need in the cloud at the point where it's then got enough information it'll basically ping you and say hey I know no I'm not a machine do you want exactly the same performance at lowest price in the cloud or do you want better performance here's two configurations pick the one you want give it your Google user ID and password it will build the security build the application servers and begin a migration for you automatically depending on the timing demand the size the box between 30 minutes and two hours later you will have a running version of your SCP system in the closet never been done before that's been performance the way it works basically it's a bit a little bit of magic but it knows how much what's the minimum amount of data we need to ship across through NSEP it knows where all the data is hidden on the box on the disk then sdb needs to run and it just ships that first and then it fills in the gaps afterwards the repair mechanism so from there on the one hand you could do lists and share and frankly our competitors have been using it to do lift and shift in the past it over some a ton of potential right for a bunch of customers we can replicate their production boxes in real time and give them 30-second RPO RTO in high availability but that done but it's like that I can now take that replicated image and I can run operations on it I can run tests on I can run QE rebuilds were you because of the Google pricing model you don't pay me in advance you pay me in arrears for only the computer time that you use so you are a QA system you've got two days worth of work to rebuild it don't shut down your QA system pay me for two days rebuild and you're done or we have integrated it directly into the SDP upgrade tools so you can pipe across your system to us and we will immediately do a test upgrade for you into s4 HANA or you see us rocky or BW an Hana whatever you want I have a customer in Canada who really jumped from ECC e6 and hazard by 5 to s4 Hana using an earlier version of the tools in 72 hours with a lot of gaps to look at in between we reckon we're gonna crush that down into under 24 hours so under 24 hours we can you can literally click on an SUV server and we will not just bring you to the cloud but we will upgrade you all the way to the latest version and we we have all the components we've done it we're pushing that through right and so what we're doing now is taken the hard work and automating that so we can get to the really cool stuff in the eye side right that's way again this is where all of us for all the hyper scalers hosts you know SV systems we want to do something that's better than that right we want to make it easy to get there but we know that in order to justify what you do we're all have seven your room app 2x or hard on right so we want to make it really easy to do that and we want to make it incredibly easy to add in AI and all the other technologies along the way that's a DES and a pricing model that nobody will be right and that's that's a pretty cool place to be I'm mighty glad to be a good place I could tell by your energy so ease of use everybody wants that you talked about just the example of invoices how they can vary so dramatically and you know whether you're a small business owner to a large enterprise there's so much complexity and and fact that was one of the things that was talked about it was this morning well yeah when how so plot I was even talking about naming conventions and how customers were starting to get confused with all of the different acquisitions SAT has done so a I what Google is doing with AI on sa piece sounds like a huge differentiator so tell us as we wrap up here what makes you know in a nutshell Google different than the other hyper scale that s AP partners with and specifically what excites you about going to market with s AP at the base level your Google's just on a different scale from everybody right we are effectively put 25% of the internet if you look at our own assets we we own dark fiber that's equivalent to about 4% of the entire caballo sorry four times the entire capacity of the Internet right MA so my ability to deliver to those customers at scale and up performance levels just unchallenged in this space so you know it's a Google clearly is excelled in a lot of different areas it's been credibly starting to bring that to SVP and carry through but you're right that the the the value add ultimately isn't just the hey I can I can run you and I can run you better write the value add is so March we announced direct innovation rihana and Google bigquery when you're talking about bigquery right massive datasets that you can know Bridge to Hana if you're a retailer this is one last example I can now join all the ad tech data Google has so I can tell you all the agile currently run in Google once we march was being viewed anonymized in clusters so you can't tell the original consumers but I know that data and directly worded to bigquery and I can join at stp so I can now say you are advertising in this area let's being clicked on but I know you don't have the inventory to actually support the advertising so I want you to move advertising somewhere else right and so I can do that manually rename when I had any I to that the potential is is incredible right we've only just started so ya know next time I want the cube we'll see where we're at but it's a it's a fun place to be speaking the next time gasps have a conference coming up Google next is coming up at the end of July yeah it's we have a lot of announcements through probably the rest of the year right there's a lot of stuff going on as we come to massive scale in the SUV space so yeah anyone who's interested in this stuff especially even if you're just interesting the I stuff Google next is the place to be so sounds like it I'm expecting some big things from that based on what you talked about on how enthusiastic you are about being at Google Paul thanks so much for joining Keith and me back on the cube and we look forward to talking to you again Thanks thank you for watching the cube Lisa Martin with Keith Townsend @s AP Safire 2018 thanks for watching

>> From Orlando, Florida, it's theCUBE. Covering SAP SAPPHIRE NOW 2018. Brought to you by NetApp. Welcome to theCUBE, we are on the ground in the NetApp booth at SAP SAPPHIRE 2018. I'm Lisa Martin, I'm hanging out with Keith Townsend. Today we are joined by RJ Bibby, who is the SAP Global Alliance Chief of Staff at NetApp. RJ, welcome to theCUBE. >> Thank you, we're so glad you guys are here. >> So, this, is a huge event. There are, I've heard 20,000 attendees live, but they were saying at the keynote this morning, Bill McDermott was, over a million people are expected to engage with the SAP experience both in person and online, that's enormous, enormous. SAP and NetApp have been partners for 17 plus years now. Right, you've got thousands of customers that run SAP on NetApp. What's current with the partnership? What's going on there from your perspective? >> Well thanks Lisa, thanks Keith. But first off I want to thank you all for being here. We're ecstatic for having theCUBE in our booth. We haven't been back here as a sponsor in a couple years. So being a platinum sponsor, 40 people here on the ground from all over the world. Like you said, we're about 26,000 people this week. So, really busy, we're in our 18th year. This year, as a partnership with SAP. To answer that question, it's really exciting. We have a very unique partnership with SAP. It's a true 360 partnership, and what I mean by that. One, we co-innovate together. So we're doing co-innovation where NetApp on SAP on NetApp. What that mean is basically a lot of the SAP products, like hybris, like Ariba, SuccessFactors are built on NetApp. We're doing co-innovation on blockchain, on HANA, IOT. So, we're really looking at that next phase of automation in data management. And we'll get into data management in a bit. We're both customers to each other. We just had our CIO met with a customer success office this morning to talk about some of the integrations of products that we're doing. Second year in a row SAP has been our largest customer. So the growth on that end is great, and then lastly the GODA market, and that's really what I really do from the alliance side. So, heavily around HANA acceleration. How we constantly are helping our customers move to HANA with our NetApp data fabric and ONTAP, our core signature products that deploy SAP. And we're very focused on industry, very focused on a global to local partner for life. We both have really warm, loyal customers. And then there's a kind of G100 strategic approach too. So that's the partnership, it's been a lot of fun. And we're gonna see where it goes in 2018. >> So RJ, talk to us a little bit more. Add some color on this relationship between Netapp and SAP in the market. NetApp, data driven company. SAP, probably the premier data analysis, analytics. We saw on stage from customer experience all the way to backend. You can't do that without a solid, robust infrastructure that's focused on SAP. What are some of the key technologies and strategy that NetApp and SAP have teamed together, bring together such successes? >> No, great question Keith. Really it goes down to the core of data. So NetApp has done a transformation the last two years, where we're gonna be now the data management company for hybrid cloud. So in that core, customers are looking to do a bunch of different things with NetApp. We want to manage, transport, analyze and protect data. A lot of data on SAP. So they're modernizing their data centers, how do we move to the hybrid cloud? With our ONTAP product, which is really a software capability, really turning into a software company in the cloud, as is SAP. So the core products of HANA, SuccessFactors, Ariba, Field Glass, Concur, all the things from an operations standpoint that's been automated for their business is kind of built on NetApp. Is built on NetApp, a lot of them. So our approach to the customer is how do we help the experience? And, we're doing that transformation internally, so we're going through it with SAP. There's lessons there. SAP did this and moved to a kind of cloud company a couple years ago on NetApp. Those are some of the core instances, but there's a modernizing a data center approach, there's a hybrid cloud. But it still just comes down to, oh my God, data runs my business. I'm really scared about it from protection. There's too much of it. How do I monetize? What are the analytics behind it? And that's what NetApp is really on the forefront of doing. Our CIO talked about this, this week. He's going to talk about it this week, about choice. What we're hearing for customers is, I need choice. I need to move my data around on PRAM, into whatever hybrid hyperscaler environment you want fast, efficient, with analytics read outs. So that's kind of the approach we're starting to take to market. I find it to be a very consultative of approach where it's Mister Customer, SAP NetApp, whoever your hybrid cloud choice is, who your SI is, the other technology partners are. We're all together collectively, almost like a innovation program for a customer approach. And that's kind of, not my secret, but one of my secrets of how we're going to market with the sales teams. >> I'm curious, NetApp is 26 years old. 26 years young maybe. I worked there I was telling you, for a few years as well. On marketing, which was awesome. Lot of evolution from a storage perspective. You say NetApp runs a SAP, SAP runs a NetApp as well. Talk to us about this maybe SAP as an influencer of the evolution of NetApp from storage company to now as you said, data authority for hybrid cloud. >> Yeah, no great question. I think it started where we wanted, we saw that software was kind of taking over. The automation, right? So it's almost like storage is a service. In my four years at NetApp, we never approach SAP as a partner or the customer talking about storage or infrastructure per se. Kind of around this data management methodology a while back. I think SAP has been an influencer internally fpr us in a couple regards. One they have the state of the art, a lot of the software operations. Things that we needed to run the business. There's been some kinks, there's some things that we probably need to customize that fit our business. NetApp's really unique, we're about 6 billion dollars, with 10,000 employees, with three business units. And we're a very unique company. The culture is awesome, we're empowered. Salespeople on the ground are empowered. Me helping run the alliance, we can be very strategic on how and what we want to do. Hey, we want to have CUBE at SAPPHIRE, absolutely do it, as an example. So, with that empowerment, we've been able to look at the best of breed in tools. And I think the tools are helping us from looking at the business and really how the customer experience. I'll give you one example on that. We're listening to our customers and how they want to transform their data, in their data on SAP. Well, I need to also be able to look at the analytics internally on okay, does my customer need a technical refresh? What are they doing on SAP? Is it SAP on Oracle? What products do they have of NetApp? Do our salespeople properly enabled on selling SAP on Cloud? Are they talking to their counterparts at the account from SAP, from a CISCO or Fujitsu, AWS, and then whatever SI. So there's a lot of complexity, there's an art and a science to it. And it's in our transformation in SAP from the tools perspectives at the core of that. >> RJ let's talk about the alliance beyond just the SAP to NetApp. This is really complex, I mean even with the tools, you know, ONTAP on the cloud, ONTAP in your data center. ONTAP kind of in the fog later, wherever you want to say that's at. That normalizes the data, it kind of validates the NetApp as the data driven company. However, when you go to an enterprise and you say that, you know what, this thing that used to live at my data center is now spread across these three different environments. It's really hard to figure out. How do SIs play a role into shaping the strategy in this alliance? Yeah, that's a whole other layer right? The complexity, 'cause I find, I came from the SI side of the house. I worked at Accenture for a really long time prior to my career in the partnership side. You know, I think they're very good from a consultative approach of hey, how do we want to design this thing? How do we want to implement it? How do we want to run it? And where does everybody's silo of stuff or technologies fall into that? I think the art part of it is hey, as NetApp or with MRSI, hey man, how do we help design with you? How do we consult the end to end approach here? I think we're the expert from an end to end data management approach. So there's some butting of heads at times depending on which SI, because they do. They have these long standing executive partnerships. There's a lot of investment from SIs at the account. I was just at a leadership conference with Accenture. And they're spending three billion dollars on three different things around automation. One, training. They can't get people, it's still about people in process. How do we get the process and tools in place? Where do we need to go merger and acquisition on the latest products? And how do we implement with that ecosystem? So I always think it's a work in progress. It's gone well, I think that's something I'd like to see us improve on. I think the SAP to NetApp partnership is advanced. In a lot of regards to that. It's like anything, it's also like when you look at salespeople internally at NetApp with our transformation. How do you get people out of the conference zone talking to their infrastructure lead, their line of business lead. And elevate to the cloud conversation. Going to the CXO, I think the Chief Security Offer is the key executive now in our sales process. Because of data protection. And that's something that we do well, and that's something they own, and I'm always trying to be creative. There might lots of dollars to protect data. How do we turn that into a whole strategy conversation with all the partners? >> So let's go a little deeper on NetApp's value propositions. You know what? Infrastructure is infrastructure, why should it matter? How do you guys differentiate between your competitors and running S4 HANA, the cloud strategy, you know what, end memory databases, storage is no longer needed, that's not true, but what's the story? >> Good question. The story for us is the ONTAP product that we have, the software because what we can do is deploy SAP really fast. Really fast, just some stats. You can get 45% project timeline savings with our deployment of SAP. The secret sauce in that is, the tools of the replication in the snapshot. When you're doing constant development ongoing maintenance, we can do snapshots in real time. That is the key thing that keeps the production going live faster. >> You know, because CICD is not something that we do, I've managed SAP for a long time. And CICD wasn't exactly a concept in SAP. So we rely on the infrastructure a lot to do. So snapshots is an amazing example of how you bring the CICD approach to something that is stayed as SAP. You can't just shut down SAP for the weekend to apply a update five times a year. >> Correct, so hours and hours of down time, where we can do it in three hours. A lot of times it's real time. I was just at a HANA Conference in Vegas and we got a lot of one on one time with customers. It was awesome, and that was the biggest things they said they need more of NetApp. And the differentiator is we're continue to expand our approach to managing the data, and I need the replication and the cloning specifically to run the production value end to end. So that's the other part of it. It's really just doing that end to end landscape management of SAP and Non-SAP workloads. The one thing that's great about the cloud part of this is you do need a lot of storage, and it's software based storage. So I think the approach in NetApp is going in the right direction. I've been working with SAP as a partner now for 12 years. I think that this is probably the best momentum I've had with SAP ever. And one of the reasons why is one, data is the story, right? What does Bill McDermott always say? Data is now the currency. Well today he was saying now trust is the currency, which is completely true too. But from the data being the currency perspective, it's now the end game for both of us. So we've kind of, in all companies, have gone into the middle. That's kind of not only the messaging, but kind of the central thing we're trying to deliver value on. And the choice, I want to keep saying the fact that customers now want choice on where they put their data. That's the thing that we're really promoting here at SAPPHIRE this week. >> Last question RJ. >> Last? All day! >> I know, I know right? Speaking of choice, you mentioned customers want choice. They do want choice. You talked about value, delivering value. From a competitive perspective, customers have choice. They've got other storage vendors they can work with. Give us your best elevator pitch. What makes NetApp and SAP different and better than say, some of those, maybe orange colored competitors? >> Sure, no, no, it's a great question. The biggest differentiator is just the fact that we are the one company out there that can provide data management in any hybrid environment. AWS, a hyperscaler, Microsoft, Google, we're doing cloud volumes just announced a Google Cloud platform. You know, we're one of the premier technology from HANA and Azure. So I think number one it's that. Secondly, we can deploy SAP really quickly, which consumes licenses. So one, the customer really likes that. Two, SAP sales loves it 'cause then it gives them a chance to go back to the customer. And then just the end to end data management that we can provide our customers value. I would say choice one. >> Awesome, well I said a few minutes ago to Keith that Bill McDermott is probably the most energetic C level that I've ever seen. Your energy level RJ, right there with his. >> You know why? 'Cause it's go time, it's SAPPHIRE day one. >> The stage might have exploded if we had them both at the same time. >> That would've been fun. >> Pyrotechnics on day one! Well RJ thank you so much, not only for visiting with Keith and me this morning. But also of having theCube in the NetApp booth at SAPPHIRE. >> We love it, we can't wait. Thanks everybody. >> I'm Lisa Martin with Keith Townsend at theCUBE on the ground at SAPPHIRE NOW day one. Stick around, we'll be right back with our next guest.

live from Las Vegas it's the cube covering service now knowledge 2018 brought to you by service now welcome back to the cubes live coverage of service now knowledge 18 we are here in Las Vegas Nevada I'm your host Rebecca Knight along with my co-host Dave allanté we are joined by John Donahoe who is the president and CEO of ServiceNow thanks so much for coming on the cube it's great to be here Rebecca so I want to talk with you a little bit about what you said on the main stage this morning you said this is your first year your anniversary of joining ServiceNow you said when you got here you could barely spell IT but when you reflect back on this year what has been sort of the biggest surprise challenges and surprises about about leading this company well I would say a couple things one I've sort of fallen in love with our customers and the challenges and opportunities they have and what I spoke about this morning this digital transformation thing even a year ago is a bit of a buzzword it's a reality for CEOs for companies and therefore for CIOs and then the second thing that is as I talked about it something very exciting is the role of the CIO the role of IT is transforming before our very eyes out of necessity because technology is here to stay technology's driving strategic change at every company can call it a digital transformation called a tech transformation and CEOs need the most technically savvy leader in the c-suite to help with that and that's often the CIO and so I think that's an enormous ly exciting opportunity for the people that are our traditional customer base and then the last thing I just I'm thrilled about is how many companies are saying that ServiceNow is a strategic platform of choice going forward far beyond just IT and so that's something to roll build upon I was struck yesterday in the Financial Analysts session you shared with us your meeting with the board yeah and you said to them look if you want to clean this thing up flip it whatever that don't hire me I'm here to build a sustainable company during company I think is what you said and the attributes of an enduring companies that are Purpose Driven they both innovate and execute they invest in talent and they have a will to win they got a fight in them a lot of good sports analogies there yeah so okay so you've set that framework where do you see this thing going in the next near term mid term and long term well we've said I think it's really important to set the aspiration of what it is you're shooting toward I've been surprised how many customers have responded well to the statement that we aspire to create a built to last company it starts with the purpose I defined our purpose and that purpose is a long term investment and our employees are already deeply resonating with the purpose and then comes the hard work the hard work of how you bring the purpose to life and our purpose and our product and the work we do with our customers all fit together you talked about automation and in many executives that we talked to kind of run away from that we don't want to talk about automation because it implies we're gonna replace humans you said hey we're at the center of automation we have to take that issue head-on what's the conversation like with the executives and customers that you talk to well the first thing is I have to think yet to look at the data which is what I've spent time doing and two things jump out one if you look at where automation is really gonna have the biggest impact it's not in any given job it's actually the third of all of our jobs that are repetitive administrative redundant right that's so we need to automate the low value-added parts of all of our jobs and then that will free our time up to be due to leverage our more creative capabilities to add more value and so if you look at it both at a micro and macro standpoint where automation is going to impact jobs it's not a given category it's more of a horizontal cut of all jobs and then secondly looking at aggregate job creation I've done a fair amount of work with James mineka the McKinsey Institute is to blow up a suit who's got to think the best objective macro study about job creation and there going to be some jobs they'll be fewer of and other jobs they'll be more of and how do we migrate the skills migration so that people have the skills for the jobs of the future one of which by the ways things like being a ServiceNow administrator you do not have to be a computer science major or an engineer to be a ServiceNow administrator you have to like technology you have to embrace technology but you can do it as a mere mortal and so we're looking at ways of how do we help retrain people to have the skills to create one of the jobs that we're creating through ServiceNow administrators John you talk to a lot of people I think five or six hundred customers know and they'll have since I met you a year ago it ServiceNow headquarters we obviously talked to a lot of people on the cube and no question every CEO the ax talked it was trying to get digital right yep they understand it but there's somewhat of a dissonance and I wonder if you sense it in and I wonder if you could talk about how ServiceNow can help wear this the c-suite gets it and they're driving for that but when you go below the line there's a lot of sometimes complacency not in our industry not in my lifetime I'll be retired by then do you hear a lot of that and how can ServiceNow help increase the urgency well I'd say I take a couple things Dave one is the c-suite gets it by not every c-suites role-modeling what's necessary without the cross-functional leadership the partnership of ITN HR and the business units then what happens by tama goes to three levels down people have functional identities and so people role model are behaving the way they see their leadership team role modeling and so if that if that c suite is embracing technology and understanding technology demands cross-functional engagement to deliver great customer experiences and employee experiences then it makes it a lot easier two three steps down the second thing I think c-suite people need to do is be able to say we take if off the table we said I talked about top-down goals most people are scared of a top-down goal the problem is if there's a not a top-down goal then people can debate if we need to make this change and how but if the CEO the c-suite says we are going to improve the employee experience and I'm setting this goal then it's when you go a level two levels down it's not if no no they said if now our job is how and so I think leadership has to do its role and I think I think the c-suite and leadership's learning how you lead and a technology enabled environment so leadership is the key and and the CEO is really leading a little suite I think the whole the whole C suite set of leaders and partnering and reaching out to one another so we I mean as you said on the main stage in many ways the technology is the easy part but what you're talking about is the hard stuff because this is the real change management and and it's human lead so what are you hearing what are you seeing and do you have any ideas for best practices I mean as you said that the the C suite needs to embrace it yes and then push that down but how do you do it what are some what are some of the things you've seen that work well here's some of the things that we're trying to do to contribute toward that because obviously we're a software platform but one is to do what I did this morning which is be more articulate about what best practice looks like what is best in class so that anyone in any organization can can go to their boss and say oh this is best practice this is best-in-class we need to emulate this and here are the returns we can get if we emulate it so one is just hold out the successes successful examples and illustrate what's required that's why I kept saying over and over this morning employee experience is not just an HR issue employee experience is not just an IT issue you need a powerful team of CIO C HR o other functional leaders and then the second thing I think is getting people on i.t to see themselves a little bit differently we have a CIO track going on upstairs with a hundred top CIOs and the whole day is around driving culture change and CIO is leader and I think good leaders they don't just allow a label to be attached to them they invest in themselves they build their skills they build change management skills communication skills and I think whether it's a CIO or IT if they're going to have the kind of transformative impact they can across the company they need to build their technical expertise along with other skill sets you heard Andrew Wilson talk about that and they need to learn to speak business and not just IT John I want to push on something that I'm discerning from you guys and get your reaction so obviously cloud you guys are born in the cloud cloud is a tailwind for you we've seen this Asif occation of business but we seem to be entering a new era moving from a cloud of remote services to one of us fabric Ubiquiti is fabric of digital services so my question is around innovation you talked about that as one of the key attributes of an enduring company what's the innovation equation going forward yeah it's not Moore's law anymore it's not cloud mobile social Big Data at least it doesn't feel that way anymore is it machine intelligence combined with cloud what do you see I think it gets down actually to what I talked about this morning user experience I think machine learning I think AI is going to be a commodity functionality we're gonna get it from AWS or Azure or Google the cloud infrastructure providers whether it's natural language processing whether it's the kind of machine learning capabilities that's that's gonna be sort of available widely then it's our job as a software platform to build that into our platform so we built machine learning capability into our platform we built chat bot functionality into our platform we built leading-edge mobile capability into our platform and again I'll call that I don't know it's the easy part but that's our job in this equation the hard job then is how you apply that to real-world use cases whether you're applying using real-world datasets specific customer data sets and real-world workflows and use cases so let me give you a small example we bought a machine learning company a year ago called DX continuum great machine learning team great machine learning technology we rebuilt it inside the ServiceNow platform okay and I don't believe a AI is a horizontal platform is I don't you know we didn't call it a name it after a a dead scientist that's out what we're gonna do and I'm not casting judgment on it but it's not a solution looking for a problem we built machine learning into our platform and then so we want to be the first user we want to use it on a specific challenge so the case we used it on our own inbound customer support we have about 800 customer support agents that serve our customers about 11 percent of their time is spent on something we call incident categorization and incident routing sounds kind of grunty terms but when summer calls with a problem we have to be able to identify what that problem is and then route it to the right person to fix the problem so 11% of our peoples time was doing that that's not a fun task so we turned on machine learning and within two weeks the machine was categorizing the issue and routing it more accurately than a human can so now what happens is our customers problems are getting solve faster and the 11% of those resources those customer support resources who are engineers in our case are focused on solving customer problems not doing what felt like an administrative task to them and so I think the actual application of machine learning the actual application in many of these these technologies it's the application that's going to matter not the invention so a lot of what you said makes it makes sense to me because you're saying that your customers are gonna be buying essentially that machine learning capability in relative and applying it in very narrow use cases to solve their business problems rather than trying to build it right and you do see some companies trying to maybe get over out over their skis and over-rotate to try to build some of that stuff that's gonna come from the technology suppliers what yours if we're doing our job the infrastructure providers the software platforms like us we're doing our job we're making it easy another small example will be mobile I talked this morning about companies everywhere need to build mobile experiences and so there one do I need to build a mobile design team a mobile coding team if you're up if you're a bank or utility or an oil and gas company or a retailer or well platforms like ours make building mobile experiences really easy for them so we're trying to build that mobile capability that design capability that Design Thinking the mobile capability into the platform so they can just get out-of-the-box functionality and they don't have to have their own mobile designers they don't have their own mobile engineers they can just be saying how do I want to use mobile inside my company and then there they're taking our mobile platform if you will and and creating mobile applications and mobile experiences that are relevant for them so your brand identity is now making work work better for people yes when you are doing your blue sky thinking about the pain points that employees feel and that job candidates feel because that's their another important part of of companies trying to keep their people happy yes what what are what do you see I mean as you said the next three to five years are going to be this the revolution is going to be in the workplace yes what do you see as sort of the biggest challenges that you want to help solve well let me just take a simple use case that that comes to mind as you mention that let's take from the time you start being recruited for a company through that let's say you get hired and get started so the recruiting process you're sending a resume and you don't know if I got in didn't get in if anyone someone may or may not contact you you may get an interview you got to find out where you're going if you're going did you get called back maybe you get an offer letter it comes you get it all set all kind of I would call an unstructured workflow let's say you get hired then the onboarding process onboarding is a classic unstructured workflow you got to go to this security to get your badge you got to go to facilities to get your desk you got to go to it2 get your laptop or mobile phone you got to get to another part of IT to get your email credentials put on you've got to enter your information into the payroll system you got to reenter your same information and pick a health care provider you got a range of the same information and and and get a in the tini system you got to do all this compliance training painting an accurate ownerís picture this is your first impression of the company you're joining now there is no reason they took my mobile phone away from me so I'm twitching there's no reason why there shouldn't be an app that says a recruit says I want to interview if the company they download the app they submit their resume based on the app we give a response in the app they say oh might my resume was accepted and I they want me to do an interview and they want me to be in Santa Clara next once at 8:00 and here's who I'm going to be meeting with and here's their background in the app then they do the interview let's say they get invited back who they're interviewing with we're inside the app okay let's say then they get an offer well then the app has more permission in the offer comes through the app you can print it or you can read it then onboarding starts onboarding can be a seamless experience it still can connect but you enter your data in once it pre fills all those systems and then in one mobile experience you're picking what's your laptop what's your healthcare system what's the bank you want your payroll in teeny to go into and all the complexity is hidden underneath it that's what we have in the consumer world our lives at home when you buy something on eBay all the complexities hidden when you pay with PayPal all the complexities hidden there's no reason why all the complexity can't be hidden in the recruiting and onboarding process and and so the technology's there to do it but it's managing all the workflows managing all the processes underneath so you can pull that together into a seamless experience and that's the kind of experience it's funny I have four grown kids my daughter she started working I won't say where but a major technology company and she's like dad what's up with this onboarding process why isn't it in a mobile app and the Millennials will start demanding this and so I I just think there's so much opportunity to make our lives at work feel more like our lives at home and you just described the capability that allow you to reach your aspirations of the next great enterprise software company when we think of great enterprise software companies we think of Oracle and si P you're nothing like Oracle and si P in my opinion and then of course you think of Salesforce different you know you're not a an SMB how should we be thinking about the next great enterprise software company so this I think this is a really important question Dave and I'd look at it through the eyes that what I heard from the 500 customers and here's what I heard they're embracing digital transformation they're embracing cloud they're embracing cloud at the infrastructure level figuring out their data center strategy and how much they embrace public cloud and then at the software platform level they're saying we want to have four to six strategic platforms and often it's the born in the cloud platforms often its sales force and workday and service now and maybe office 365 or Google for email or communications maybe if they have a supply chain ASAP and they're saying I want those platforms to work well together so no one platform should be claiming they can do everything each of us needs to figure out what's our role and how do we work with one another and our role ServiceNow I'm proud to say is one of those strategic platforms as I said earlier people see our capabilities as being connective tissues helping to pull those platforms together you know in the onboarding example we pull all the data sets and platforms together by the way we don't slap our brand on top because actually employees want to see their own brands they want to see their own company's brand they don't want to know what the enterprise software brand underneath it is they just wanna have a great experience and so I I don't view it I think the winning enterprise software I see a chance for Salesforce and workday and ServiceNow and Microsoft to all be winners and delivering this future for companies where you are the platform of platforms though correct but that's not and I'm being very careful the way I say it I'm not saying we're the top dog sure I'm saying what we're good at is cross-functional workflow actually it's probably the grunt 'ya stuff all those things and you're the best at it and we're the best at you are and our brand we're not we're not forcing our brand everywhere that we're doing it in service to our customers and so I just want to always be listening to what our customers want that's gonna be our North Star they're gonna guide us it always has been I know you know Fred Letty started that from the beginning and that's what we're gonna continue to do well John it's always a pleasure having you on the cube so thanks so much for coming on our show thank you very much Becky thank you Dave great to be happy John I'm Rebecca night for Dave Allante we will have more from ServiceNow knowledge 18 in just a little bit [Music]